quantlib-users — QuantLib users discussion list

Re: [Quantlib-users] New QuantLib related project: ORE Studio

[Dmitri G. <dm...@ma...>]

Hi Marco,

Great to see ORE Studio — looks like a useful project.

On the AAD side: we’ve had a working integration of AADC with ORE for
several years now — transparent AAD valuation through the standard ORE
workflow, including JIT compilation and replay for Monte Carlo paths. We
have clients running this in production.

Here’s a demo from 2022
<https://www.youtube.com/watch?v=1WN8gUF120M&utm_campaign=crm&utm_source=email&utm_medium=direct&tid=natwest-marco-craveiro-20260417-1>
showing
ORE+AADC in action.

More details: live risk architecture
<https://matlogica.com/technology/live-risk-architecture/?utm_campaign=crm&utm_source=email&utm_medium=direct&tid=natwest-marco-craveiro-20260417-2>
 and ORE LiveRisk
<https://matlogica.com/technology/ore-liverisk/?utm_campaign=crm&utm_source=email&utm_medium=direct&tid=natwest-marco-craveiro-20260417-3>
.

Happy to show you a live demo or discuss how it might fit with ORE Studio.
Kind regards,
Dmitri Goloubentsev
Head of Automatic Adjoint Differentiation,
Matlogica LTD
http://matlogica.com +447378414528
See my schedule and book <https://calendly.com/matlogica> a meeting with me


On Thu, 16 Apr 2026 at 17:12, Marco Craveiro <mar...@gm...>
wrote:

> Hi QuantLib devs,
>
> A few months ago I started a new open source project called ORE Studio:
>
> - https://github.com/OreStudio/OreStudio
>
> This email is my first outreach attempt. ORE Studio's ultimate
> objective will be to provide a CRUD and service layer for ORE [1].
> Note that the project is not directly affiliated with ORE, but it is
> so named because it only targets ORE and no other risk engine. It may
> be subject to change, If Roland or anyone from ORE has any qualms with
> it.
>
> Releases are done when we accumulate enough commits; I am about to
> release v0.0.16 over the next few days. I recommend always using main
> and updating very frequently, as the code tends to move around a lot.
>
> I also feel compelled to provide some warnings and caveats:
>
> - Though I've been at it for over 6 months, it is *very* early days
> for the project; many things don't work as expected, or don't work at
> all. The code is stabilising somewhat but, in truth, *anything* may be
> subject to change. For example, we just recently transitioned from a
> monolith to a service-based architecture
> - The documentation is not great, and it is certainly not up-to-date.
> - ORE Studio is *100%* developed with AI - more specifically Claude
> Code [2] - so the code is not exactly the absolute best. One of my
> objectives with ORE Studio is to test the AI state of the art in anger
> - the other being testing the suitability of ORE for Intraday Risk
> management. If you do not like AI, I'm rather afraid ORE Studio won't
> be to your liking. Contributions without AI are, of course, very
> welcome.
>
> The main focus for the eventual v1.0.0 will be to get the end-to-end
> workflow working correctly across the entire ORE instrument inventory
> (as per ORE samples):
>
> - UI for entering trades (with instrument-specific handling), managing
> curves, surfaces etc - effectively UIs covering all of ORE input data.
> - reporting subsystem to create batches, send them for valuation;
> - compute subsystem to value batches;
> - UI to view report results.
>
> The skeleton for this structure is now in place, but has many (many)
> limitations across the board. In fairness, I have somewhat rushed the
> outreach; my objective was to get something basic finished by the ORE
> user meet up but, given it is on the 11th of May, I ran out of time. I
> wanted those interested to have time to have a play prior to the
> event, and hopefully gather some feedback from the community at the
> meetup, as to the usefulness of this project outside my use cases.
>
> Finally, I do not wish to abuse Luigi's patience and send too many
> emails about ORE Studio on the QuantLib malinglist :-) so, if you want
> to ask any questions or just have a chat, can I please ask you to pop
> over to our discord:
>
> https://discord.gg/gcrYsjW3pd
>
> You can also contact me directly, either over email or Twitter. And,
> as with any GitHub project, you can raise issues, discussions etc.
>
> I look forward to hearing from you. Many thanks for your time.
> --
> Marco Craveiro, PhD
>
> about: https://mcraveiro.github.io/about.html
> site: https://mcraveiro.github.io/
> twitter: https://twitter.com/MarcoCraveiro
>
> [1] https://github.com/OpenSourceRisk/Engine
> [2] https://claude.com/product/claude-code
>
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] New QuantLib related project: ORE Studio

[Marco C. <mar...@gm...>]

Hi Dmitri,

> Great to see ORE Studio — looks like a useful project.

Thanks for the kind words. Also, as requested by a few people, I
created a quick video overview of the project; it gives a better idea
of what we're aiming for (with apologies in advance for the sound
quality):

https://youtu.be/sgf7ybOpWxE

> On the AAD side: we’ve had a working integration of AADC with ORE for several years now — transparent AAD valuation through the standard ORE workflow, including JIT compilation and replay for Monte Carlo paths. We have clients running this in production.
>
> Here’s a demo from 2022 showing ORE+AADC in action.
>
> More details: live risk architecture and ORE LiveRisk.

Whilst I am very interested, I must confess I have not made much
progress on the AAD front so far. I've had a brief conversation with
Jorg regarding their QuantLibAAD approach [1]. It looked extremely
interesting but required further work at my end to consume it. ORE
Studio works best with something that "looks" like an ORE binary. On
the other hand, from the links you provided [2, 3], ORE + AADC appears
to integrate seamlessly with ORE Studio, requiring only config changes
(the manifest of the compute package).

> Happy to show you a live demo or discuss how it might fit with ORE Studio.

I'll contact you directly - seems very promising and should be easy to
test. Thanks very much for reaching out.

Cheers
--
Marco Craveiro, PhD

about: https://mcraveiro.github.io/about.html
site: https://mcraveiro.github.io/
twitter: https://twitter.com/MarcoCraveiro

[1] https://sourceforge.net/p/quantlib/mailman/message/59293906/
[2] https://matlogica.com/technology/live-risk-architecture
[3] https://matlogica.com/technology/ore-liverisk

[Quantlib-users] QuantLib 1.42.1 released

[Luigi B. <lui...@gm...>]

QuantLib 1.42.1 is now available for download at <
https://www.quantlib.org/download.shtml>; precompiled binaries are also
available from PyPI and NuGet for Python and C# respectively.

This is a bug-fix release for the recently released QuantLib 1.42, removing
a regression in the Gsr class possibly leading to an infinite notification
loop.  Thanks to Aleksis Ali Raza for the heads-up.

If you have any problems with this release, please report them here on the
QuantLib mailing list (<qua...@li...>) or open a
GitHub issue at <https://github.com/lballabio/quantlib/issues>.

[Quantlib-users] New QuantLib related project: ORE Studio

[Marco C. <mar...@gm...>]

Hi QuantLib devs,

A few months ago I started a new open source project called ORE Studio:

- https://github.com/OreStudio/OreStudio

This email is my first outreach attempt. ORE Studio's ultimate
objective will be to provide a CRUD and service layer for ORE [1].
Note that the project is not directly affiliated with ORE, but it is
so named because it only targets ORE and no other risk engine. It may
be subject to change, If Roland or anyone from ORE has any qualms with
it.

Releases are done when we accumulate enough commits; I am about to
release v0.0.16 over the next few days. I recommend always using main
and updating very frequently, as the code tends to move around a lot.

I also feel compelled to provide some warnings and caveats:

- Though I've been at it for over 6 months, it is *very* early days
for the project; many things don't work as expected, or don't work at
all. The code is stabilising somewhat but, in truth, *anything* may be
subject to change. For example, we just recently transitioned from a
monolith to a service-based architecture
- The documentation is not great, and it is certainly not up-to-date.
- ORE Studio is *100%* developed with AI - more specifically Claude
Code [2] - so the code is not exactly the absolute best. One of my
objectives with ORE Studio is to test the AI state of the art in anger
- the other being testing the suitability of ORE for Intraday Risk
management. If you do not like AI, I'm rather afraid ORE Studio won't
be to your liking. Contributions without AI are, of course, very
welcome.

The main focus for the eventual v1.0.0 will be to get the end-to-end
workflow working correctly across the entire ORE instrument inventory
(as per ORE samples):

- UI for entering trades (with instrument-specific handling), managing
curves, surfaces etc - effectively UIs covering all of ORE input data.
- reporting subsystem to create batches, send them for valuation;
- compute subsystem to value batches;
- UI to view report results.

The skeleton for this structure is now in place, but has many (many)
limitations across the board. In fairness, I have somewhat rushed the
outreach; my objective was to get something basic finished by the ORE
user meet up but, given it is on the 11th of May, I ran out of time. I
wanted those interested to have time to have a play prior to the
event, and hopefully gather some feedback from the community at the
meetup, as to the usefulness of this project outside my use cases.

Finally, I do not wish to abuse Luigi's patience and send too many
emails about ORE Studio on the QuantLib malinglist :-) so, if you want
to ask any questions or just have a chat, can I please ask you to pop
over to our discord:

https://discord.gg/gcrYsjW3pd

You can also contact me directly, either over email or Twitter. And,
as with any GitHub project, you can raise issues, discussions etc.

I look forward to hearing from you. Many thanks for your time.
--
Marco Craveiro, PhD

about: https://mcraveiro.github.io/about.html
site: https://mcraveiro.github.io/
twitter: https://twitter.com/MarcoCraveiro

[1] https://github.com/OpenSourceRisk/Engine
[2] https://claude.com/product/claude-code

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Luigi B. <lui...@gm...>]

Thanks!

On Thu, Apr 16, 2026 at 11:32 AM Aleksis Ali Raza <
ale...@go...> wrote:

> yes, the results tie with v1.40 after the code fix in 1.42.
>
> here's a comparison run for the test script i sent earlier.
>
> --- GSR live setValue bump test v1.42---
> base rate: 0.04875825
> base npv : 16.787915348295005
>
> setValue -> 0.04885825
> npv = 17.005154111913548
>
> setValue -> 0.04865825
> npv = 16.57057870270632
>
> setValue -> 0.04875825
> npv = 16.787915348295005
>
> starting stress loop...
> iter 000  rate=0.04885825  npv=17.005154111913548
> iter 020  rate=0.04885825  npv=17.005154111913548
> iter 040  rate=0.04885825  npv=17.005154111913548
> iter 060  rate=0.04885825  npv=17.005154111913548
> iter 080  rate=0.04885825  npv=17.005154111913548
> iter 100  rate=0.04885825  npv=17.005154111913548
> iter 120  rate=0.04885825  npv=17.005154111913548
> iter 140  rate=0.04885825  npv=17.005154111913548
> iter 160  rate=0.04885825  npv=17.005154111913548
> iter 180  rate=0.04885825  npv=17.005154111913548
>
> restored rate: 0.04875825
> restored npv : 16.787915348295005
>
>
> --- GSR live setValue bump test v1.40 ---
> base rate: 0.04875825
> base npv : 16.787915348295005
>
> setValue -> 0.04885825
> npv = 17.005154111913548
>
> setValue -> 0.04865825
> npv = 16.57057870270632
>
> setValue -> 0.04875825
> npv = 16.787915348295005
>
> starting stress loop...
> iter 000  rate=0.04885825  npv=17.005154111913548
> iter 020  rate=0.04885825  npv=17.005154111913548
> iter 040  rate=0.04885825  npv=17.005154111913548
> iter 060  rate=0.04885825  npv=17.005154111913548
> iter 080  rate=0.04885825  npv=17.005154111913548
> iter 100  rate=0.04885825  npv=17.005154111913548
> iter 120  rate=0.04885825  npv=17.005154111913548
> iter 140  rate=0.04885825  npv=17.005154111913548
> iter 160  rate=0.04885825  npv=17.005154111913548
> iter 180  rate=0.04885825  npv=17.005154111913548
>
> restored rate: 0.04875825
> restored npv : 16.787915348295005
>
>
> On Apr 16, 2026, at 12:38, Luigi Ballabio <lui...@gm...>
> wrote:
>
> May you check that it also reprices correctly after the bump? That is,
> that you get the same numerical results as 1.40?
>
> On Thu, Apr 16, 2026 at 9:04 AM Luigi Ballabio <lui...@gm...>
> wrote:
>
>> Thanks for the heads-up.  Looks like I need to release a 1.42.1...
>>
>> Luigi
>>
>>
>> On Thu, Apr 16, 2026 at 8:57 AM Aleksis Ali Raza via QuantLib-users <
>> qua...@li...> wrote:
>>
>>> Yep, ditching registerWith(stateProcess_); works fine as well and is the
>>> cleaner fix.
>>>
>>> thanks.
>>>
>>> On Apr 16, 2026, at 11:34, Peter Caspers <pca...@gm...> wrote:
>>>
>>> Thanks. Since Gsr manages its stateProcess_, it does not need to and
>>> should not register with it as an Observer? I will take a closer look, but
>>> if you could test this change on your end, that would be valuable input.
>>> Best, Peter
>>>
>>>
>>> Aleksis Ali Raza via QuantLib-users <
>>> qua...@li...> schrieb am Do. 16. Apr. 2026 um
>>> 08:04:
>>>
>>>> Just to follow up on this, it's this line:
>>>>
>>>> void Gsr::update() {
>>>>     if (stateProcess_ != nullptr) {
>>>>
>>>> ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>>>>
>>>> *ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();*
>>>>     }
>>>>     LazyObject::update();
>>>> }
>>>>
>>>> in the gsr class that seems to be the cause. in my case, commenting out
>>>> that line out and rebuilding from source fixes the problem of my gsr-based
>>>> swaption risk runs.
>>>>
>>>> That line by itself looks pretty harmless but i think
>>>>
>>>> registerWith(stateProcess_);
>>>>
>>>> further down leads into some sort of feedback loop (setValue() →
>>>>  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →
>>>>  Gsr::update() →  …) and ultimately a seg fault.
>>>>
>>>> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <
>>>> ale...@go...> wrote:
>>>>
>>>> Hi,
>>>>
>>>> I’m encountering what appears to be a regression or stability issue in
>>>> QuantLib 1.42 related to the GSR (Gaussian short rate) model and the
>>>> observer framework.
>>>>
>>>> Calling SimpleQuote.setValue() on a quote that is observed by a term
>>>> structure used in a Gsr model causes a segmentation fault.
>>>>
>>>> This occurs even in a minimal standalone script (no threading, no
>>>> external frameworks).
>>>>
>>>> Minimal reproduction
>>>>
>>>> The following sequence is sufficient:
>>>>         1.      Create a SimpleQuote
>>>>         2.      Build a FlatForward curve using that quote
>>>>         3.      Construct a Gsr model from that term structure
>>>>         4.      Attach a Gaussian1dSwaptionEngine
>>>>         5.      Call setValue() on the original SimpleQuote
>>>>
>>>> This results in a segmentation fault.
>>>>
>>>> I have modified the Python/examples/bermudan_swaption.py file and
>>>> attached it below to illustrate (along with error log below it).
>>>>
>>>> Has something been changed in how one shud enquire / update quote
>>>> objects that I missed? I am coming from 1.40, where this was not an issue.
>>>> I am running python 3.14.4 on macOS 26.1.4 and using a source build of
>>>> Quantlib/SWIG.
>>>>
>>>> Thanks, Aleksis
>>>>
>>>> # ---
>>>> # jupyter:
>>>> #   jupytext:
>>>> #     formats: py:light
>>>> #     text_representation:
>>>> #       extension: .py
>>>> #       format_name: light
>>>> #       format_version: '1.5'
>>>> #       jupytext_version: 1.4.2
>>>> #   kernelspec:
>>>> #     display_name: Python 3
>>>> #     language: python
>>>> #     name: python3
>>>> # ---
>>>>
>>>> # # Bermudan swaptions
>>>> #
>>>> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
>>>> #
>>>> # This file is part of QuantLib, a free-software/open-source library
>>>> # for financial quantitative analysts and developers - https://www.quantlib.org/
>>>> #
>>>> # QuantLib is free software: you can redistribute it and/or modify it under the
>>>> # terms of the QuantLib license.  You should have received a copy of the
>>>> # license along with this program; if not, please email
>>>> # <qua...@li...>. The license is also available online at
>>>> # <https://www.quantlib.org/license.shtml>.
>>>> #
>>>> # This program is distributed in the hope that it will be useful, but WITHOUT
>>>> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
>>>> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>>>>
>>>> import QuantLib as ql
>>>> import pandas as pd
>>>>
>>>> # ### Setup
>>>>
>>>> todaysDate = ql.Date(15, ql.February, 2002)
>>>> ql.Settings.instance().evaluationDate = todaysDate
>>>> calendar = ql.TARGET()
>>>> settlementDate = ql.Date(19, ql.February, 2002)
>>>>
>>>>
>>>> def calibrate(model, helpers, l, name):
>>>>     print("Model: %s" % name)
>>>>
>>>>     method = ql.Simplex(l)
>>>>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>>>>
>>>>     print("Parameters: %s" % model.params())
>>>>
>>>>     totalError = 0.0
>>>>     data = []
>>>>     for swaption, helper in zip(swaptionVols, helpers):
>>>>         maturity, length, vol = swaption
>>>>         NPV = helper.modelValue()
>>>>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>>>>         error = implied - vol
>>>>         totalError += abs(error)
>>>>         data.append((maturity, length, vol, implied, error))
>>>>     averageError = totalError / len(helpers)
>>>>
>>>>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>>>>
>>>>     print("Average error: %.4f" % averageError)
>>>>
>>>>
>>>> # ### Market data
>>>>
>>>> swaptionVols = [
>>>>     # maturity,          length,             volatility
>>>>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>>>>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>>>>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>>>>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>>>>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
>>>> ]
>>>>
>>>> # This is a flat yield term structure implying a 1x5 swap at 5%.
>>>>
>>>> rateQuote = ql.SimpleQuote(0.04875825)
>>>> rate = ql.QuoteHandle(rateQuote)
>>>> termStructure = ql.YieldTermStructureHandle(
>>>>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
>>>> )
>>>>
>>>> # Define the ATM/OTM/ITM swaps:
>>>>
>>>> swapEngine = ql.DiscountingSwapEngine(termStructure)
>>>>
>>>> fixedLegFrequency = ql.Annual
>>>> fixedLegTenor = ql.Period(1, ql.Years)
>>>> fixedLegConvention = ql.Unadjusted
>>>> floatingLegConvention = ql.ModifiedFollowing
>>>> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
>>>> floatingLegFrequency = ql.Semiannual
>>>> floatingLegTenor = ql.Period(6, ql.Months)
>>>>
>>>> payFixed = ql.Swap.Payer
>>>> fixingDays = 2
>>>> index = ql.Euribor6M(termStructure)
>>>> floatingLegDayCounter = index.dayCounter()
>>>>
>>>> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
>>>> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>>>>
>>>> fixedSchedule = ql.Schedule(
>>>>     swapStart,
>>>>     swapEnd,
>>>>     fixedLegTenor,
>>>>     calendar,
>>>>     fixedLegConvention,
>>>>     fixedLegConvention,
>>>>     ql.DateGeneration.Forward,
>>>>     False,
>>>> )
>>>> floatingSchedule = ql.Schedule(
>>>>     swapStart,
>>>>     swapEnd,
>>>>     floatingLegTenor,
>>>>     calendar,
>>>>     floatingLegConvention,
>>>>     floatingLegConvention,
>>>>     ql.DateGeneration.Forward,
>>>>     False,
>>>> )
>>>>
>>>> dummy = ql.VanillaSwap(
>>>>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
>>>> )
>>>> dummy.setPricingEngine(swapEngine)
>>>> atmRate = dummy.fairRate()
>>>>
>>>> atmSwap = ql.VanillaSwap(
>>>>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>> )
>>>>
>>>> otmSwap = ql.VanillaSwap(
>>>>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>> )
>>>>
>>>> itmSwap = ql.VanillaSwap(
>>>>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>> )
>>>>
>>>> atmSwap.setPricingEngine(swapEngine)
>>>> otmSwap.setPricingEngine(swapEngine)
>>>> itmSwap.setPricingEngine(swapEngine)
>>>>
>>>> helpers = [
>>>>     ql.SwaptionHelper(
>>>>         maturity,
>>>>         length,
>>>>         ql.makeQuoteHandle(vol),
>>>>         index,
>>>>         index.tenor(),
>>>>         index.dayCounter(),
>>>>         index.dayCounter(),
>>>>         termStructure,
>>>>     )
>>>>     for maturity, length, vol in swaptionVols
>>>> ]
>>>>
>>>> times = {}
>>>> for h in helpers:
>>>>     for t in h.times():
>>>>         times[t] = 1
>>>> times = sorted(times.keys())
>>>>
>>>> grid = ql.TimeGrid(times, 30)
>>>>
>>>> G2model = ql.G2(termStructure)
>>>> HWmodel = ql.HullWhite(termStructure)
>>>> HWmodel2 = ql.HullWhite(termStructure)
>>>> BKmodel = ql.BlackKarasinski(termStructure)
>>>>
>>>> # ### Calibrations
>>>>
>>>> for h in helpers:
>>>>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
>>>> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>>>>
>>>> for h in helpers:
>>>>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
>>>> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>>>>
>>>> for h in helpers:
>>>>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
>>>> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>>>>
>>>> for h in helpers:
>>>>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
>>>> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>>>>
>>>>
>>>> # ### Price Bermudan swaptions on defined swaps
>>>>
>>>> bermudanDates = [d for d in fixedSchedule][:-1]
>>>> exercise = ql.BermudanExercise(bermudanDates)
>>>>
>>>> atmSwaption = ql.Swaption(atmSwap, exercise)
>>>> otmSwaption = ql.Swaption(otmSwap, exercise)
>>>> itmSwaption = ql.Swaption(itmSwap, exercise)
>>>>
>>>> data = []
>>>>
>>>> # +
>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>>
>>>> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>
>>>> # +
>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>>
>>>> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>
>>>> # +
>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>>
>>>> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>
>>>> # +
>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>>
>>>> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>> # -
>>>>
>>>> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>>>>
>>>> # ### GSR live setValue bump test
>>>>
>>>> import faulthandler
>>>> faulthandler.enable(all_threads=True)
>>>>
>>>> print("\n--- GSR live setValue bump test ---")
>>>>
>>>> # Minimal European swaption test on the same underlying ATM swap
>>>> euroExercise = ql.EuropeanExercise(swapStart)
>>>> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>>>>
>>>> # For a European GSR setup: no step dates, one sigma, one reversion
>>>> stepDates = []
>>>> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>>> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>>>
>>>> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
>>>> gsrEngine = ql.Gaussian1dSwaptionEngine(
>>>>     gsr,
>>>>     64,     # integration points
>>>>     7.0,    # std devs
>>>>     True,
>>>>     False,
>>>>     termStructure
>>>> )
>>>> euroSwaption.setPricingEngine(gsrEngine)
>>>>
>>>> base_rate = 0.04875825
>>>>
>>>> print("base rate:", base_rate)
>>>> print("base npv :", euroSwaption.NPV())
>>>>
>>>> # Single bump up/down
>>>> for bump in [1e-4, -1e-4, 0.0]:
>>>>     bumped = base_rate + bump
>>>>     print(f"\nsetValue -> {bumped:.8f}")
>>>>     rateQuote.setValue(bumped)
>>>>     print("npv =", euroSwaption.NPV())
>>>>
>>>> # Stress loop: repeatedly bump the observed quote and reprice
>>>> print("\nstarting stress loop...")
>>>> for i in range(200):
>>>>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>>>>     rateQuote.setValue(bumped)
>>>>     npv = euroSwaption.NPV()
>>>>     if i % 20 == 0:
>>>>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>>>>
>>>> # Restore
>>>> rateQuote.setValue(base_rate)
>>>> print("\nrestored rate:", base_rate)
>>>> print("restored npv :", euroSwaption.NPV())
>>>>
>>>>
>>>> Log:
>>>>
>>>> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
>>>> Model: G2 (analytic formulae)
>>>> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>>>>   maturity length  volatility   implied     error
>>>> 0       1Y     5Y      0.1148  0.101974 -0.012826
>>>> 1       2Y     4Y      0.1108  0.105611 -0.005189
>>>> 2       3Y     3Y      0.1070  0.106950 -0.000050
>>>> 3       4Y     2Y      0.1021  0.107873  0.005773
>>>> 4       5Y     1Y      0.1000  0.108556  0.008556
>>>> Average error: 0.0065
>>>> Model: Hull-White (analytic formulae)
>>>> Parameters: [ 0.046379; 0.00586848 ]
>>>>   maturity length  volatility   implied     error
>>>> 0       1Y     5Y      0.1148  0.106223 -0.008577
>>>> 1       2Y     4Y      0.1108  0.106292 -0.004508
>>>> 2       3Y     3Y      0.1070  0.106338 -0.000662
>>>> 3       4Y     2Y      0.1021  0.106439  0.004339
>>>> 4       5Y     1Y      0.1000  0.106609  0.006609
>>>> Average error: 0.0049
>>>> Model: Hull-White (numerical calibration)
>>>> Parameters: [ 0.0559314; 0.00609858 ]
>>>>   maturity length  volatility   implied     error
>>>> 0       1Y     5Y      0.1148  0.102941 -0.011859
>>>> 1       2Y     4Y      0.1108  0.105452 -0.005348
>>>> 2       3Y     3Y      0.1070  0.106560 -0.000440
>>>> 3       4Y     2Y      0.1021  0.107365  0.005265
>>>> 4       5Y     1Y      0.1000  0.108223  0.008223
>>>> Average error: 0.0062
>>>> Model: Black-Karasinski (numerical calibration)
>>>> Parameters: [ 0.0442393; 0.120656 ]
>>>>   maturity length  volatility   implied     error
>>>> 0       1Y     5Y      0.1148  0.103094 -0.011706
>>>> 1       2Y     4Y      0.1108  0.105637 -0.005163
>>>> 2       3Y     3Y      0.1070  0.106656 -0.000344
>>>> 3       4Y     2Y      0.1021  0.107333  0.005233
>>>> 4       5Y     1Y      0.1000  0.108028  0.008028
>>>> Average error: 0.0061
>>>>           model  in-the-money  at-the-money  out-of-the-money
>>>> 0   G2 analytic     42.919620     14.581452          3.443306
>>>> 1   HW analytic     42.245449     12.924016          2.512541
>>>> 2  HW numerical     42.340450     13.141040          2.614228
>>>> 3  BK numerical     41.805673     13.012682          3.271628
>>>>
>>>> --- GSR live setValue bump test ---
>>>> base rate: 0.04875825
>>>> base npv : 16.787915348295005
>>>>
>>>> setValue -> 0.04885825
>>>> Fatal Python error: Segmentation fault
>>>>
>>>> Current thread 0x00000001f0afd8c0 (most recent call first):
>>>>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>>>>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>>>>
>>>> Current thread's C stack trace (most recent call first):
>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>>>>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>   <truncated rest of calls>
>>>>
>>>> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
>>>> Segmentation fault: 11
>>>>
>>>>
>>>> _______________________________________________
>>>> QuantLib-users mailing list
>>>> Qua...@li...
>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>>
>>>
>>> _______________________________________________
>>> QuantLib-users mailing list
>>> Qua...@li...
>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>
>>
>

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Aleksis A. R. <ale...@go...>]

yes, the results tie with v1.40 after the code fix in 1.42. 

here's a comparison run for the test script i sent earlier.

--- GSR live setValue bump test v1.42---
base rate: 0.04875825
base npv : 16.787915348295005

setValue -> 0.04885825
npv = 17.005154111913548

setValue -> 0.04865825
npv = 16.57057870270632

setValue -> 0.04875825
npv = 16.787915348295005

starting stress loop...
iter 000  rate=0.04885825  npv=17.005154111913548
iter 020  rate=0.04885825  npv=17.005154111913548
iter 040  rate=0.04885825  npv=17.005154111913548
iter 060  rate=0.04885825  npv=17.005154111913548
iter 080  rate=0.04885825  npv=17.005154111913548
iter 100  rate=0.04885825  npv=17.005154111913548
iter 120  rate=0.04885825  npv=17.005154111913548
iter 140  rate=0.04885825  npv=17.005154111913548
iter 160  rate=0.04885825  npv=17.005154111913548
iter 180  rate=0.04885825  npv=17.005154111913548

restored rate: 0.04875825
restored npv : 16.787915348295005


--- GSR live setValue bump test v1.40 ---
base rate: 0.04875825
base npv : 16.787915348295005

setValue -> 0.04885825
npv = 17.005154111913548

setValue -> 0.04865825
npv = 16.57057870270632

setValue -> 0.04875825
npv = 16.787915348295005

starting stress loop...
iter 000  rate=0.04885825  npv=17.005154111913548
iter 020  rate=0.04885825  npv=17.005154111913548
iter 040  rate=0.04885825  npv=17.005154111913548
iter 060  rate=0.04885825  npv=17.005154111913548
iter 080  rate=0.04885825  npv=17.005154111913548
iter 100  rate=0.04885825  npv=17.005154111913548
iter 120  rate=0.04885825  npv=17.005154111913548
iter 140  rate=0.04885825  npv=17.005154111913548
iter 160  rate=0.04885825  npv=17.005154111913548
iter 180  rate=0.04885825  npv=17.005154111913548

restored rate: 0.04875825
restored npv : 16.787915348295005


> On Apr 16, 2026, at 12:38, Luigi Ballabio <lui...@gm...> wrote:
> 
> May you check that it also reprices correctly after the bump? That is, that you get the same numerical results as 1.40?
> 
> On Thu, Apr 16, 2026 at 9:04 AM Luigi Ballabio <lui...@gm... <mailto:lui...@gm...>> wrote:
>> Thanks for the heads-up.  Looks like I need to release a 1.42.1...
>> 
>> Luigi
>> 
>> 
>> On Thu, Apr 16, 2026 at 8:57 AM Aleksis Ali Raza via QuantLib-users <qua...@li... <mailto:qua...@li...>> wrote:
>>> Yep, ditching registerWith(stateProcess_); works fine as well and is the cleaner fix. 
>>> 
>>> thanks.
>>> 
>>>> On Apr 16, 2026, at 11:34, Peter Caspers <pca...@gm... <mailto:pca...@gm...>> wrote:
>>>> 
>>>> Thanks. Since Gsr manages its stateProcess_, it does not need to and should not register with it as an Observer? I will take a closer look, but if you could test this change on your end, that would be valuable input.
>>>> Best, Peter
>>>> 
>>>> 
>>>> Aleksis Ali Raza via QuantLib-users <qua...@li... <mailto:qua...@li...>> schrieb am Do. 16. Apr. 2026 um 08:04:
>>>>> Just to follow up on this, it's this line:
>>>>> 
>>>>> void Gsr::update() {
>>>>>     if (stateProcess_ != nullptr) {
>>>>>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>>>>>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();
>>>>>     }
>>>>>     LazyObject::update();
>>>>> }
>>>>> 
>>>>> in the gsr class that seems to be the cause. in my case, commenting out that line out and rebuilding from source fixes the problem of my gsr-based swaption risk runs.
>>>>> 
>>>>> That line by itself looks pretty harmless but i think
>>>>> registerWith(stateProcess_);
>>>>> 
>>>>> further down leads into some sort of feedback loop (setValue() →  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →  Gsr::update() →  …) and ultimately a seg fault.
>>>>> 
>>>>> 
>>>>>> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go... <mailto:ale...@go...>> wrote:
>>>>>> 
>>>>>> Hi,
>>>>>> 
>>>>>> I’m encountering what appears to be a regression or stability issue in QuantLib 1.42 related to the GSR (Gaussian short rate) model and the observer framework.
>>>>>> 
>>>>>> Calling SimpleQuote.setValue() on a quote that is observed by a term structure used in a Gsr model causes a segmentation fault.
>>>>>> 
>>>>>> This occurs even in a minimal standalone script (no threading, no external frameworks).
>>>>>> 
>>>>>> Minimal reproduction
>>>>>> 
>>>>>> The following sequence is sufficient:
>>>>>>         1.      Create a SimpleQuote
>>>>>>         2.      Build a FlatForward curve using that quote
>>>>>>         3.      Construct a Gsr model from that term structure
>>>>>>         4.      Attach a Gaussian1dSwaptionEngine
>>>>>>         5.      Call setValue() on the original SimpleQuote
>>>>>> 
>>>>>> This results in a segmentation fault. 
>>>>>> 
>>>>>> I have modified the Python/examples/bermudan_swaption.py file and attached it below to illustrate (along with error log below it).
>>>>>> 
>>>>>> Has something been changed in how one shud enquire / update quote objects that I missed? I am coming from 1.40, where this was not an issue. I am running python 3.14.4 on macOS 26.1.4 and using a source build of Quantlib/SWIG.
>>>>>> 
>>>>>> Thanks, Aleksis
>>>>>> 
>>>>>> # ---
>>>>>> # jupyter:
>>>>>> #   jupytext:
>>>>>> #     formats: py:light
>>>>>> #     text_representation:
>>>>>> #       extension: .py
>>>>>> #       format_name: light
>>>>>> #       format_version: '1.5'
>>>>>> #       jupytext_version: 1.4.2
>>>>>> #   kernelspec:
>>>>>> #     display_name: Python 3
>>>>>> #     language: python
>>>>>> #     name: python3
>>>>>> # ---
>>>>>> 
>>>>>> # # Bermudan swaptions
>>>>>> #
>>>>>> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
>>>>>> #
>>>>>> # This file is part of QuantLib, a free-software/open-source library
>>>>>> # for financial quantitative analysts and developers - https://www.quantlib.org/
>>>>>> #
>>>>>> # QuantLib is free software: you can redistribute it and/or modify it under the
>>>>>> # terms of the QuantLib license.  You should have received a copy of the
>>>>>> # license along with this program; if not, please email
>>>>>> # <qua...@li... <mailto:qua...@li...>>. The license is also available online at
>>>>>> # <https://www.quantlib.org/license.shtml>.
>>>>>> #
>>>>>> # This program is distributed in the hope that it will be useful, but WITHOUT
>>>>>> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
>>>>>> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>>>>>> 
>>>>>> import QuantLib as ql
>>>>>> import pandas as pd
>>>>>> 
>>>>>> # ### Setup
>>>>>> 
>>>>>> todaysDate = ql.Date(15, ql.February, 2002)
>>>>>> ql.Settings.instance().evaluationDate = todaysDate
>>>>>> calendar = ql.TARGET()
>>>>>> settlementDate = ql.Date(19, ql.February, 2002)
>>>>>> 
>>>>>> 
>>>>>> def calibrate(model, helpers, l, name):
>>>>>>     print("Model: %s" % name)
>>>>>> 
>>>>>>     method = ql.Simplex(l)
>>>>>>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>>>>>> 
>>>>>>     print("Parameters: %s" % model.params())
>>>>>> 
>>>>>>     totalError = 0.0
>>>>>>     data = []
>>>>>>     for swaption, helper in zip(swaptionVols, helpers):
>>>>>>         maturity, length, vol = swaption
>>>>>>         NPV = helper.modelValue()
>>>>>>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>>>>>>         error = implied - vol
>>>>>>         totalError += abs(error)
>>>>>>         data.append((maturity, length, vol, implied, error))
>>>>>>     averageError = totalError / len(helpers)
>>>>>> 
>>>>>>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>>>>>> 
>>>>>>     print("Average error: %.4f" % averageError)
>>>>>> 
>>>>>> 
>>>>>> # ### Market data
>>>>>> 
>>>>>> swaptionVols = [
>>>>>>     # maturity,          length,             volatility
>>>>>>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>>>>>>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>>>>>>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>>>>>>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>>>>>>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
>>>>>> ]
>>>>>> 
>>>>>> # This is a flat yield term structure implying a 1x5 swap at 5%.
>>>>>> 
>>>>>> rateQuote = ql.SimpleQuote(0.04875825)
>>>>>> rate = ql.QuoteHandle(rateQuote)
>>>>>> termStructure = ql.YieldTermStructureHandle(
>>>>>>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
>>>>>> )
>>>>>> 
>>>>>> # Define the ATM/OTM/ITM swaps:
>>>>>> 
>>>>>> swapEngine = ql.DiscountingSwapEngine(termStructure)
>>>>>> 
>>>>>> fixedLegFrequency = ql.Annual
>>>>>> fixedLegTenor = ql.Period(1, ql.Years)
>>>>>> fixedLegConvention = ql.Unadjusted
>>>>>> floatingLegConvention = ql.ModifiedFollowing
>>>>>> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
>>>>>> floatingLegFrequency = ql.Semiannual
>>>>>> floatingLegTenor = ql.Period(6, ql.Months)
>>>>>> 
>>>>>> payFixed = ql.Swap.Payer
>>>>>> fixingDays = 2
>>>>>> index = ql.Euribor6M(termStructure)
>>>>>> floatingLegDayCounter = index.dayCounter()
>>>>>> 
>>>>>> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
>>>>>> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>>>>>> 
>>>>>> fixedSchedule = ql.Schedule(
>>>>>>     swapStart,
>>>>>>     swapEnd,
>>>>>>     fixedLegTenor,
>>>>>>     calendar,
>>>>>>     fixedLegConvention,
>>>>>>     fixedLegConvention,
>>>>>>     ql.DateGeneration.Forward,
>>>>>>     False,
>>>>>> )
>>>>>> floatingSchedule = ql.Schedule(
>>>>>>     swapStart,
>>>>>>     swapEnd,
>>>>>>     floatingLegTenor,
>>>>>>     calendar,
>>>>>>     floatingLegConvention,
>>>>>>     floatingLegConvention,
>>>>>>     ql.DateGeneration.Forward,
>>>>>>     False,
>>>>>> )
>>>>>> 
>>>>>> dummy = ql.VanillaSwap(
>>>>>>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
>>>>>> )
>>>>>> dummy.setPricingEngine(swapEngine)
>>>>>> atmRate = dummy.fairRate()
>>>>>> 
>>>>>> atmSwap = ql.VanillaSwap(
>>>>>>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>>>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>>>> )
>>>>>> 
>>>>>> otmSwap = ql.VanillaSwap(
>>>>>>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>>>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>>>> )
>>>>>> 
>>>>>> itmSwap = ql.VanillaSwap(
>>>>>>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>>>>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>>>>> )
>>>>>> 
>>>>>> atmSwap.setPricingEngine(swapEngine)
>>>>>> otmSwap.setPricingEngine(swapEngine)
>>>>>> itmSwap.setPricingEngine(swapEngine)
>>>>>> 
>>>>>> helpers = [
>>>>>>     ql.SwaptionHelper(
>>>>>>         maturity,
>>>>>>         length,
>>>>>>         ql.makeQuoteHandle(vol),
>>>>>>         index,
>>>>>>         index.tenor(),
>>>>>>         index.dayCounter(),
>>>>>>         index.dayCounter(),
>>>>>>         termStructure,
>>>>>>     )
>>>>>>     for maturity, length, vol in swaptionVols
>>>>>> ]
>>>>>> 
>>>>>> times = {}
>>>>>> for h in helpers:
>>>>>>     for t in h.times():
>>>>>>         times[t] = 1
>>>>>> times = sorted(times.keys())
>>>>>> 
>>>>>> grid = ql.TimeGrid(times, 30)
>>>>>> 
>>>>>> G2model = ql.G2(termStructure)
>>>>>> HWmodel = ql.HullWhite(termStructure)
>>>>>> HWmodel2 = ql.HullWhite(termStructure)
>>>>>> BKmodel = ql.BlackKarasinski(termStructure)
>>>>>> 
>>>>>> # ### Calibrations
>>>>>> 
>>>>>> for h in helpers:
>>>>>>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
>>>>>> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>>>>>> 
>>>>>> for h in helpers:
>>>>>>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
>>>>>> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>>>>>> 
>>>>>> for h in helpers:
>>>>>>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
>>>>>> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>>>>>> 
>>>>>> for h in helpers:
>>>>>>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
>>>>>> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>>>>>> 
>>>>>> 
>>>>>> # ### Price Bermudan swaptions on defined swaps
>>>>>> 
>>>>>> bermudanDates = [d for d in fixedSchedule][:-1]
>>>>>> exercise = ql.BermudanExercise(bermudanDates)
>>>>>> 
>>>>>> atmSwaption = ql.Swaption(atmSwap, exercise)
>>>>>> otmSwaption = ql.Swaption(otmSwap, exercise)
>>>>>> itmSwaption = ql.Swaption(itmSwap, exercise)
>>>>>> 
>>>>>> data = []
>>>>>> 
>>>>>> # +
>>>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>>>> 
>>>>>> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>>> 
>>>>>> # +
>>>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>>>> 
>>>>>> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>>> 
>>>>>> # +
>>>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>>>> 
>>>>>> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>>> 
>>>>>> # +
>>>>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>>>> 
>>>>>> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>>>> # -
>>>>>> 
>>>>>> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>>>>>> 
>>>>>> # ### GSR live setValue bump test
>>>>>> 
>>>>>> import faulthandler
>>>>>> faulthandler.enable(all_threads=True)
>>>>>> 
>>>>>> print("\n--- GSR live setValue bump test ---")
>>>>>> 
>>>>>> # Minimal European swaption test on the same underlying ATM swap
>>>>>> euroExercise = ql.EuropeanExercise(swapStart)
>>>>>> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>>>>>> 
>>>>>> # For a European GSR setup: no step dates, one sigma, one reversion
>>>>>> stepDates = []
>>>>>> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>>>>> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>>>>> 
>>>>>> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
>>>>>> gsrEngine = ql.Gaussian1dSwaptionEngine(
>>>>>>     gsr,
>>>>>>     64,     # integration points
>>>>>>     7.0,    # std devs
>>>>>>     True,
>>>>>>     False,
>>>>>>     termStructure
>>>>>> )
>>>>>> euroSwaption.setPricingEngine(gsrEngine)
>>>>>> 
>>>>>> base_rate = 0.04875825
>>>>>> 
>>>>>> print("base rate:", base_rate)
>>>>>> print("base npv :", euroSwaption.NPV())
>>>>>> 
>>>>>> # Single bump up/down
>>>>>> for bump in [1e-4, -1e-4, 0.0]:
>>>>>>     bumped = base_rate + bump
>>>>>>     print(f"\nsetValue -> {bumped:.8f}")
>>>>>>     rateQuote.setValue(bumped)
>>>>>>     print("npv =", euroSwaption.NPV())
>>>>>> 
>>>>>> # Stress loop: repeatedly bump the observed quote and reprice
>>>>>> print("\nstarting stress loop...")
>>>>>> for i in range(200):
>>>>>>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>>>>>>     rateQuote.setValue(bumped)
>>>>>>     npv = euroSwaption.NPV()
>>>>>>     if i % 20 == 0:
>>>>>>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>>>>>> 
>>>>>> # Restore
>>>>>> rateQuote.setValue(base_rate)
>>>>>> print("\nrestored rate:", base_rate)
>>>>>> print("restored npv :", euroSwaption.NPV())
>>>>>> 
>>>>>> Log:
>>>>>> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
>>>>>> Model: G2 (analytic formulae)
>>>>>> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>>>>>>   maturity length  volatility   implied     error
>>>>>> 0       1Y     5Y      0.1148  0.101974 -0.012826
>>>>>> 1       2Y     4Y      0.1108  0.105611 -0.005189
>>>>>> 2       3Y     3Y      0.1070  0.106950 -0.000050
>>>>>> 3       4Y     2Y      0.1021  0.107873  0.005773
>>>>>> 4       5Y     1Y      0.1000  0.108556  0.008556
>>>>>> Average error: 0.0065
>>>>>> Model: Hull-White (analytic formulae)
>>>>>> Parameters: [ 0.046379; 0.00586848 ]
>>>>>>   maturity length  volatility   implied     error
>>>>>> 0       1Y     5Y      0.1148  0.106223 -0.008577
>>>>>> 1       2Y     4Y      0.1108  0.106292 -0.004508
>>>>>> 2       3Y     3Y      0.1070  0.106338 -0.000662
>>>>>> 3       4Y     2Y      0.1021  0.106439  0.004339
>>>>>> 4       5Y     1Y      0.1000  0.106609  0.006609
>>>>>> Average error: 0.0049
>>>>>> Model: Hull-White (numerical calibration)
>>>>>> Parameters: [ 0.0559314; 0.00609858 ]
>>>>>>   maturity length  volatility   implied     error
>>>>>> 0       1Y     5Y      0.1148  0.102941 -0.011859
>>>>>> 1       2Y     4Y      0.1108  0.105452 -0.005348
>>>>>> 2       3Y     3Y      0.1070  0.106560 -0.000440
>>>>>> 3       4Y     2Y      0.1021  0.107365  0.005265
>>>>>> 4       5Y     1Y      0.1000  0.108223  0.008223
>>>>>> Average error: 0.0062
>>>>>> Model: Black-Karasinski (numerical calibration)
>>>>>> Parameters: [ 0.0442393; 0.120656 ]
>>>>>>   maturity length  volatility   implied     error
>>>>>> 0       1Y     5Y      0.1148  0.103094 -0.011706
>>>>>> 1       2Y     4Y      0.1108  0.105637 -0.005163
>>>>>> 2       3Y     3Y      0.1070  0.106656 -0.000344
>>>>>> 3       4Y     2Y      0.1021  0.107333  0.005233
>>>>>> 4       5Y     1Y      0.1000  0.108028  0.008028
>>>>>> Average error: 0.0061
>>>>>>           model  in-the-money  at-the-money  out-of-the-money
>>>>>> 0   G2 analytic     42.919620     14.581452          3.443306
>>>>>> 1   HW analytic     42.245449     12.924016          2.512541
>>>>>> 2  HW numerical     42.340450     13.141040          2.614228
>>>>>> 3  BK numerical     41.805673     13.012682          3.271628
>>>>>> 
>>>>>> --- GSR live setValue bump test ---
>>>>>> base rate: 0.04875825
>>>>>> base npv : 16.787915348295005
>>>>>> 
>>>>>> setValue -> 0.04885825
>>>>>> Fatal Python error: Segmentation fault
>>>>>> 
>>>>>> Current thread 0x00000001f0afd8c0 (most recent call first):
>>>>>>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>>>>>>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>>>>>> 
>>>>>> Current thread's C stack trace (most recent call first):
>>>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>>>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>>>>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>>>>>>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>>>>   <truncated rest of calls>
>>>>>> 
>>>>>> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
>>>>>> Segmentation fault: 11
>>>>> 
>>>>> _______________________________________________
>>>>> QuantLib-users mailing list
>>>>> Qua...@li... <mailto:Qua...@li...>
>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>> 
>>> _______________________________________________
>>> QuantLib-users mailing list
>>> Qua...@li... <mailto:Qua...@li...>
>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Luigi B. <lui...@gm...>]

May you check that it also reprices correctly after the bump? That is, that
you get the same numerical results as 1.40?

On Thu, Apr 16, 2026 at 9:04 AM Luigi Ballabio <lui...@gm...>
wrote:

> Thanks for the heads-up.  Looks like I need to release a 1.42.1...
>
> Luigi
>
>
> On Thu, Apr 16, 2026 at 8:57 AM Aleksis Ali Raza via QuantLib-users <
> qua...@li...> wrote:
>
>> Yep, ditching registerWith(stateProcess_); works fine as well and is the
>> cleaner fix.
>>
>> thanks.
>>
>> On Apr 16, 2026, at 11:34, Peter Caspers <pca...@gm...> wrote:
>>
>> Thanks. Since Gsr manages its stateProcess_, it does not need to and
>> should not register with it as an Observer? I will take a closer look, but
>> if you could test this change on your end, that would be valuable input.
>> Best, Peter
>>
>>
>> Aleksis Ali Raza via QuantLib-users <qua...@li...>
>> schrieb am Do. 16. Apr. 2026 um 08:04:
>>
>>> Just to follow up on this, it's this line:
>>>
>>> void Gsr::update() {
>>>     if (stateProcess_ != nullptr) {
>>>
>>> ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>>>
>>> *ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();*
>>>     }
>>>     LazyObject::update();
>>> }
>>>
>>> in the gsr class that seems to be the cause. in my case, commenting out
>>> that line out and rebuilding from source fixes the problem of my gsr-based
>>> swaption risk runs.
>>>
>>> That line by itself looks pretty harmless but i think
>>>
>>> registerWith(stateProcess_);
>>>
>>> further down leads into some sort of feedback loop (setValue() →
>>>  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →
>>>  Gsr::update() →  …) and ultimately a seg fault.
>>>
>>> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go...>
>>> wrote:
>>>
>>> Hi,
>>>
>>> I’m encountering what appears to be a regression or stability issue in
>>> QuantLib 1.42 related to the GSR (Gaussian short rate) model and the
>>> observer framework.
>>>
>>> Calling SimpleQuote.setValue() on a quote that is observed by a term
>>> structure used in a Gsr model causes a segmentation fault.
>>>
>>> This occurs even in a minimal standalone script (no threading, no
>>> external frameworks).
>>>
>>> Minimal reproduction
>>>
>>> The following sequence is sufficient:
>>>         1.      Create a SimpleQuote
>>>         2.      Build a FlatForward curve using that quote
>>>         3.      Construct a Gsr model from that term structure
>>>         4.      Attach a Gaussian1dSwaptionEngine
>>>         5.      Call setValue() on the original SimpleQuote
>>>
>>> This results in a segmentation fault.
>>>
>>> I have modified the Python/examples/bermudan_swaption.py file and
>>> attached it below to illustrate (along with error log below it).
>>>
>>> Has something been changed in how one shud enquire / update quote
>>> objects that I missed? I am coming from 1.40, where this was not an issue.
>>> I am running python 3.14.4 on macOS 26.1.4 and using a source build of
>>> Quantlib/SWIG.
>>>
>>> Thanks, Aleksis
>>>
>>> # ---
>>> # jupyter:
>>> #   jupytext:
>>> #     formats: py:light
>>> #     text_representation:
>>> #       extension: .py
>>> #       format_name: light
>>> #       format_version: '1.5'
>>> #       jupytext_version: 1.4.2
>>> #   kernelspec:
>>> #     display_name: Python 3
>>> #     language: python
>>> #     name: python3
>>> # ---
>>>
>>> # # Bermudan swaptions
>>> #
>>> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
>>> #
>>> # This file is part of QuantLib, a free-software/open-source library
>>> # for financial quantitative analysts and developers - https://www.quantlib.org/
>>> #
>>> # QuantLib is free software: you can redistribute it and/or modify it under the
>>> # terms of the QuantLib license.  You should have received a copy of the
>>> # license along with this program; if not, please email
>>> # <qua...@li...>. The license is also available online at
>>> # <https://www.quantlib.org/license.shtml>.
>>> #
>>> # This program is distributed in the hope that it will be useful, but WITHOUT
>>> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
>>> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>>>
>>> import QuantLib as ql
>>> import pandas as pd
>>>
>>> # ### Setup
>>>
>>> todaysDate = ql.Date(15, ql.February, 2002)
>>> ql.Settings.instance().evaluationDate = todaysDate
>>> calendar = ql.TARGET()
>>> settlementDate = ql.Date(19, ql.February, 2002)
>>>
>>>
>>> def calibrate(model, helpers, l, name):
>>>     print("Model: %s" % name)
>>>
>>>     method = ql.Simplex(l)
>>>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>>>
>>>     print("Parameters: %s" % model.params())
>>>
>>>     totalError = 0.0
>>>     data = []
>>>     for swaption, helper in zip(swaptionVols, helpers):
>>>         maturity, length, vol = swaption
>>>         NPV = helper.modelValue()
>>>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>>>         error = implied - vol
>>>         totalError += abs(error)
>>>         data.append((maturity, length, vol, implied, error))
>>>     averageError = totalError / len(helpers)
>>>
>>>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>>>
>>>     print("Average error: %.4f" % averageError)
>>>
>>>
>>> # ### Market data
>>>
>>> swaptionVols = [
>>>     # maturity,          length,             volatility
>>>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>>>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>>>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>>>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>>>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
>>> ]
>>>
>>> # This is a flat yield term structure implying a 1x5 swap at 5%.
>>>
>>> rateQuote = ql.SimpleQuote(0.04875825)
>>> rate = ql.QuoteHandle(rateQuote)
>>> termStructure = ql.YieldTermStructureHandle(
>>>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
>>> )
>>>
>>> # Define the ATM/OTM/ITM swaps:
>>>
>>> swapEngine = ql.DiscountingSwapEngine(termStructure)
>>>
>>> fixedLegFrequency = ql.Annual
>>> fixedLegTenor = ql.Period(1, ql.Years)
>>> fixedLegConvention = ql.Unadjusted
>>> floatingLegConvention = ql.ModifiedFollowing
>>> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
>>> floatingLegFrequency = ql.Semiannual
>>> floatingLegTenor = ql.Period(6, ql.Months)
>>>
>>> payFixed = ql.Swap.Payer
>>> fixingDays = 2
>>> index = ql.Euribor6M(termStructure)
>>> floatingLegDayCounter = index.dayCounter()
>>>
>>> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
>>> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>>>
>>> fixedSchedule = ql.Schedule(
>>>     swapStart,
>>>     swapEnd,
>>>     fixedLegTenor,
>>>     calendar,
>>>     fixedLegConvention,
>>>     fixedLegConvention,
>>>     ql.DateGeneration.Forward,
>>>     False,
>>> )
>>> floatingSchedule = ql.Schedule(
>>>     swapStart,
>>>     swapEnd,
>>>     floatingLegTenor,
>>>     calendar,
>>>     floatingLegConvention,
>>>     floatingLegConvention,
>>>     ql.DateGeneration.Forward,
>>>     False,
>>> )
>>>
>>> dummy = ql.VanillaSwap(
>>>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>> dummy.setPricingEngine(swapEngine)
>>> atmRate = dummy.fairRate()
>>>
>>> atmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>>
>>> otmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>>
>>> itmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>>
>>> atmSwap.setPricingEngine(swapEngine)
>>> otmSwap.setPricingEngine(swapEngine)
>>> itmSwap.setPricingEngine(swapEngine)
>>>
>>> helpers = [
>>>     ql.SwaptionHelper(
>>>         maturity,
>>>         length,
>>>         ql.makeQuoteHandle(vol),
>>>         index,
>>>         index.tenor(),
>>>         index.dayCounter(),
>>>         index.dayCounter(),
>>>         termStructure,
>>>     )
>>>     for maturity, length, vol in swaptionVols
>>> ]
>>>
>>> times = {}
>>> for h in helpers:
>>>     for t in h.times():
>>>         times[t] = 1
>>> times = sorted(times.keys())
>>>
>>> grid = ql.TimeGrid(times, 30)
>>>
>>> G2model = ql.G2(termStructure)
>>> HWmodel = ql.HullWhite(termStructure)
>>> HWmodel2 = ql.HullWhite(termStructure)
>>> BKmodel = ql.BlackKarasinski(termStructure)
>>>
>>> # ### Calibrations
>>>
>>> for h in helpers:
>>>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
>>> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>>>
>>> for h in helpers:
>>>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
>>> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>>>
>>> for h in helpers:
>>>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
>>> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>>>
>>> for h in helpers:
>>>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
>>> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>>>
>>>
>>> # ### Price Bermudan swaptions on defined swaps
>>>
>>> bermudanDates = [d for d in fixedSchedule][:-1]
>>> exercise = ql.BermudanExercise(bermudanDates)
>>>
>>> atmSwaption = ql.Swaption(atmSwap, exercise)
>>> otmSwaption = ql.Swaption(otmSwap, exercise)
>>> itmSwaption = ql.Swaption(itmSwap, exercise)
>>>
>>> data = []
>>>
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>>
>>> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>>
>>> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>>
>>> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>>
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>>
>>> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>> # -
>>>
>>> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>>>
>>> # ### GSR live setValue bump test
>>>
>>> import faulthandler
>>> faulthandler.enable(all_threads=True)
>>>
>>> print("\n--- GSR live setValue bump test ---")
>>>
>>> # Minimal European swaption test on the same underlying ATM swap
>>> euroExercise = ql.EuropeanExercise(swapStart)
>>> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>>>
>>> # For a European GSR setup: no step dates, one sigma, one reversion
>>> stepDates = []
>>> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>>
>>> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
>>> gsrEngine = ql.Gaussian1dSwaptionEngine(
>>>     gsr,
>>>     64,     # integration points
>>>     7.0,    # std devs
>>>     True,
>>>     False,
>>>     termStructure
>>> )
>>> euroSwaption.setPricingEngine(gsrEngine)
>>>
>>> base_rate = 0.04875825
>>>
>>> print("base rate:", base_rate)
>>> print("base npv :", euroSwaption.NPV())
>>>
>>> # Single bump up/down
>>> for bump in [1e-4, -1e-4, 0.0]:
>>>     bumped = base_rate + bump
>>>     print(f"\nsetValue -> {bumped:.8f}")
>>>     rateQuote.setValue(bumped)
>>>     print("npv =", euroSwaption.NPV())
>>>
>>> # Stress loop: repeatedly bump the observed quote and reprice
>>> print("\nstarting stress loop...")
>>> for i in range(200):
>>>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>>>     rateQuote.setValue(bumped)
>>>     npv = euroSwaption.NPV()
>>>     if i % 20 == 0:
>>>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>>>
>>> # Restore
>>> rateQuote.setValue(base_rate)
>>> print("\nrestored rate:", base_rate)
>>> print("restored npv :", euroSwaption.NPV())
>>>
>>>
>>> Log:
>>>
>>> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
>>> Model: G2 (analytic formulae)
>>> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.101974 -0.012826
>>> 1       2Y     4Y      0.1108  0.105611 -0.005189
>>> 2       3Y     3Y      0.1070  0.106950 -0.000050
>>> 3       4Y     2Y      0.1021  0.107873  0.005773
>>> 4       5Y     1Y      0.1000  0.108556  0.008556
>>> Average error: 0.0065
>>> Model: Hull-White (analytic formulae)
>>> Parameters: [ 0.046379; 0.00586848 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.106223 -0.008577
>>> 1       2Y     4Y      0.1108  0.106292 -0.004508
>>> 2       3Y     3Y      0.1070  0.106338 -0.000662
>>> 3       4Y     2Y      0.1021  0.106439  0.004339
>>> 4       5Y     1Y      0.1000  0.106609  0.006609
>>> Average error: 0.0049
>>> Model: Hull-White (numerical calibration)
>>> Parameters: [ 0.0559314; 0.00609858 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.102941 -0.011859
>>> 1       2Y     4Y      0.1108  0.105452 -0.005348
>>> 2       3Y     3Y      0.1070  0.106560 -0.000440
>>> 3       4Y     2Y      0.1021  0.107365  0.005265
>>> 4       5Y     1Y      0.1000  0.108223  0.008223
>>> Average error: 0.0062
>>> Model: Black-Karasinski (numerical calibration)
>>> Parameters: [ 0.0442393; 0.120656 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.103094 -0.011706
>>> 1       2Y     4Y      0.1108  0.105637 -0.005163
>>> 2       3Y     3Y      0.1070  0.106656 -0.000344
>>> 3       4Y     2Y      0.1021  0.107333  0.005233
>>> 4       5Y     1Y      0.1000  0.108028  0.008028
>>> Average error: 0.0061
>>>           model  in-the-money  at-the-money  out-of-the-money
>>> 0   G2 analytic     42.919620     14.581452          3.443306
>>> 1   HW analytic     42.245449     12.924016          2.512541
>>> 2  HW numerical     42.340450     13.141040          2.614228
>>> 3  BK numerical     41.805673     13.012682          3.271628
>>>
>>> --- GSR live setValue bump test ---
>>> base rate: 0.04875825
>>> base npv : 16.787915348295005
>>>
>>> setValue -> 0.04885825
>>> Fatal Python error: Segmentation fault
>>>
>>> Current thread 0x00000001f0afd8c0 (most recent call first):
>>>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>>>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>>>
>>> Current thread's C stack trace (most recent call first):
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>>>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   <truncated rest of calls>
>>>
>>> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
>>> Segmentation fault: 11
>>>
>>>
>>> _______________________________________________
>>> QuantLib-users mailing list
>>> Qua...@li...
>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>
>>
>> _______________________________________________
>> QuantLib-users mailing list
>> Qua...@li...
>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>
>

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Luigi B. <lui...@gm...>]

Thanks for the heads-up.  Looks like I need to release a 1.42.1...

Luigi


On Thu, Apr 16, 2026 at 8:57 AM Aleksis Ali Raza via QuantLib-users <
qua...@li...> wrote:

> Yep, ditching registerWith(stateProcess_); works fine as well and is the
> cleaner fix.
>
> thanks.
>
> On Apr 16, 2026, at 11:34, Peter Caspers <pca...@gm...> wrote:
>
> Thanks. Since Gsr manages its stateProcess_, it does not need to and
> should not register with it as an Observer? I will take a closer look, but
> if you could test this change on your end, that would be valuable input.
> Best, Peter
>
>
> Aleksis Ali Raza via QuantLib-users <qua...@li...>
> schrieb am Do. 16. Apr. 2026 um 08:04:
>
>> Just to follow up on this, it's this line:
>>
>> void Gsr::update() {
>>     if (stateProcess_ != nullptr) {
>>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>>
>> *ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();*
>>     }
>>     LazyObject::update();
>> }
>>
>> in the gsr class that seems to be the cause. in my case, commenting out
>> that line out and rebuilding from source fixes the problem of my gsr-based
>> swaption risk runs.
>>
>> That line by itself looks pretty harmless but i think
>>
>> registerWith(stateProcess_);
>>
>> further down leads into some sort of feedback loop (setValue() →
>>  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →
>>  Gsr::update() →  …) and ultimately a seg fault.
>>
>> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go...>
>> wrote:
>>
>> Hi,
>>
>> I’m encountering what appears to be a regression or stability issue in
>> QuantLib 1.42 related to the GSR (Gaussian short rate) model and the
>> observer framework.
>>
>> Calling SimpleQuote.setValue() on a quote that is observed by a term
>> structure used in a Gsr model causes a segmentation fault.
>>
>> This occurs even in a minimal standalone script (no threading, no
>> external frameworks).
>>
>> Minimal reproduction
>>
>> The following sequence is sufficient:
>>         1.      Create a SimpleQuote
>>         2.      Build a FlatForward curve using that quote
>>         3.      Construct a Gsr model from that term structure
>>         4.      Attach a Gaussian1dSwaptionEngine
>>         5.      Call setValue() on the original SimpleQuote
>>
>> This results in a segmentation fault.
>>
>> I have modified the Python/examples/bermudan_swaption.py file and
>> attached it below to illustrate (along with error log below it).
>>
>> Has something been changed in how one shud enquire / update quote objects
>> that I missed? I am coming from 1.40, where this was not an issue. I am
>> running python 3.14.4 on macOS 26.1.4 and using a source build of
>> Quantlib/SWIG.
>>
>> Thanks, Aleksis
>>
>> # ---
>> # jupyter:
>> #   jupytext:
>> #     formats: py:light
>> #     text_representation:
>> #       extension: .py
>> #       format_name: light
>> #       format_version: '1.5'
>> #       jupytext_version: 1.4.2
>> #   kernelspec:
>> #     display_name: Python 3
>> #     language: python
>> #     name: python3
>> # ---
>>
>> # # Bermudan swaptions
>> #
>> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
>> #
>> # This file is part of QuantLib, a free-software/open-source library
>> # for financial quantitative analysts and developers - https://www.quantlib.org/
>> #
>> # QuantLib is free software: you can redistribute it and/or modify it under the
>> # terms of the QuantLib license.  You should have received a copy of the
>> # license along with this program; if not, please email
>> # <qua...@li...>. The license is also available online at
>> # <https://www.quantlib.org/license.shtml>.
>> #
>> # This program is distributed in the hope that it will be useful, but WITHOUT
>> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
>> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>>
>> import QuantLib as ql
>> import pandas as pd
>>
>> # ### Setup
>>
>> todaysDate = ql.Date(15, ql.February, 2002)
>> ql.Settings.instance().evaluationDate = todaysDate
>> calendar = ql.TARGET()
>> settlementDate = ql.Date(19, ql.February, 2002)
>>
>>
>> def calibrate(model, helpers, l, name):
>>     print("Model: %s" % name)
>>
>>     method = ql.Simplex(l)
>>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>>
>>     print("Parameters: %s" % model.params())
>>
>>     totalError = 0.0
>>     data = []
>>     for swaption, helper in zip(swaptionVols, helpers):
>>         maturity, length, vol = swaption
>>         NPV = helper.modelValue()
>>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>>         error = implied - vol
>>         totalError += abs(error)
>>         data.append((maturity, length, vol, implied, error))
>>     averageError = totalError / len(helpers)
>>
>>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>>
>>     print("Average error: %.4f" % averageError)
>>
>>
>> # ### Market data
>>
>> swaptionVols = [
>>     # maturity,          length,             volatility
>>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
>> ]
>>
>> # This is a flat yield term structure implying a 1x5 swap at 5%.
>>
>> rateQuote = ql.SimpleQuote(0.04875825)
>> rate = ql.QuoteHandle(rateQuote)
>> termStructure = ql.YieldTermStructureHandle(
>>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
>> )
>>
>> # Define the ATM/OTM/ITM swaps:
>>
>> swapEngine = ql.DiscountingSwapEngine(termStructure)
>>
>> fixedLegFrequency = ql.Annual
>> fixedLegTenor = ql.Period(1, ql.Years)
>> fixedLegConvention = ql.Unadjusted
>> floatingLegConvention = ql.ModifiedFollowing
>> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
>> floatingLegFrequency = ql.Semiannual
>> floatingLegTenor = ql.Period(6, ql.Months)
>>
>> payFixed = ql.Swap.Payer
>> fixingDays = 2
>> index = ql.Euribor6M(termStructure)
>> floatingLegDayCounter = index.dayCounter()
>>
>> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
>> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>>
>> fixedSchedule = ql.Schedule(
>>     swapStart,
>>     swapEnd,
>>     fixedLegTenor,
>>     calendar,
>>     fixedLegConvention,
>>     fixedLegConvention,
>>     ql.DateGeneration.Forward,
>>     False,
>> )
>> floatingSchedule = ql.Schedule(
>>     swapStart,
>>     swapEnd,
>>     floatingLegTenor,
>>     calendar,
>>     floatingLegConvention,
>>     floatingLegConvention,
>>     ql.DateGeneration.Forward,
>>     False,
>> )
>>
>> dummy = ql.VanillaSwap(
>>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
>> )
>> dummy.setPricingEngine(swapEngine)
>> atmRate = dummy.fairRate()
>>
>> atmSwap = ql.VanillaSwap(
>>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>> )
>>
>> otmSwap = ql.VanillaSwap(
>>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>> )
>>
>> itmSwap = ql.VanillaSwap(
>>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>> )
>>
>> atmSwap.setPricingEngine(swapEngine)
>> otmSwap.setPricingEngine(swapEngine)
>> itmSwap.setPricingEngine(swapEngine)
>>
>> helpers = [
>>     ql.SwaptionHelper(
>>         maturity,
>>         length,
>>         ql.makeQuoteHandle(vol),
>>         index,
>>         index.tenor(),
>>         index.dayCounter(),
>>         index.dayCounter(),
>>         termStructure,
>>     )
>>     for maturity, length, vol in swaptionVols
>> ]
>>
>> times = {}
>> for h in helpers:
>>     for t in h.times():
>>         times[t] = 1
>> times = sorted(times.keys())
>>
>> grid = ql.TimeGrid(times, 30)
>>
>> G2model = ql.G2(termStructure)
>> HWmodel = ql.HullWhite(termStructure)
>> HWmodel2 = ql.HullWhite(termStructure)
>> BKmodel = ql.BlackKarasinski(termStructure)
>>
>> # ### Calibrations
>>
>> for h in helpers:
>>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
>> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>>
>> for h in helpers:
>>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
>> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>>
>> for h in helpers:
>>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
>> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>>
>> for h in helpers:
>>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
>> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>>
>>
>> # ### Price Bermudan swaptions on defined swaps
>>
>> bermudanDates = [d for d in fixedSchedule][:-1]
>> exercise = ql.BermudanExercise(bermudanDates)
>>
>> atmSwaption = ql.Swaption(atmSwap, exercise)
>> otmSwaption = ql.Swaption(otmSwap, exercise)
>> itmSwaption = ql.Swaption(itmSwap, exercise)
>>
>> data = []
>>
>> # +
>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>
>> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>
>> # +
>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>
>> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>
>> # +
>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>
>> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>
>> # +
>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>
>> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>> # -
>>
>> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>>
>> # ### GSR live setValue bump test
>>
>> import faulthandler
>> faulthandler.enable(all_threads=True)
>>
>> print("\n--- GSR live setValue bump test ---")
>>
>> # Minimal European swaption test on the same underlying ATM swap
>> euroExercise = ql.EuropeanExercise(swapStart)
>> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>>
>> # For a European GSR setup: no step dates, one sigma, one reversion
>> stepDates = []
>> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>
>> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
>> gsrEngine = ql.Gaussian1dSwaptionEngine(
>>     gsr,
>>     64,     # integration points
>>     7.0,    # std devs
>>     True,
>>     False,
>>     termStructure
>> )
>> euroSwaption.setPricingEngine(gsrEngine)
>>
>> base_rate = 0.04875825
>>
>> print("base rate:", base_rate)
>> print("base npv :", euroSwaption.NPV())
>>
>> # Single bump up/down
>> for bump in [1e-4, -1e-4, 0.0]:
>>     bumped = base_rate + bump
>>     print(f"\nsetValue -> {bumped:.8f}")
>>     rateQuote.setValue(bumped)
>>     print("npv =", euroSwaption.NPV())
>>
>> # Stress loop: repeatedly bump the observed quote and reprice
>> print("\nstarting stress loop...")
>> for i in range(200):
>>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>>     rateQuote.setValue(bumped)
>>     npv = euroSwaption.NPV()
>>     if i % 20 == 0:
>>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>>
>> # Restore
>> rateQuote.setValue(base_rate)
>> print("\nrestored rate:", base_rate)
>> print("restored npv :", euroSwaption.NPV())
>>
>>
>> Log:
>>
>> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
>> Model: G2 (analytic formulae)
>> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>>   maturity length  volatility   implied     error
>> 0       1Y     5Y      0.1148  0.101974 -0.012826
>> 1       2Y     4Y      0.1108  0.105611 -0.005189
>> 2       3Y     3Y      0.1070  0.106950 -0.000050
>> 3       4Y     2Y      0.1021  0.107873  0.005773
>> 4       5Y     1Y      0.1000  0.108556  0.008556
>> Average error: 0.0065
>> Model: Hull-White (analytic formulae)
>> Parameters: [ 0.046379; 0.00586848 ]
>>   maturity length  volatility   implied     error
>> 0       1Y     5Y      0.1148  0.106223 -0.008577
>> 1       2Y     4Y      0.1108  0.106292 -0.004508
>> 2       3Y     3Y      0.1070  0.106338 -0.000662
>> 3       4Y     2Y      0.1021  0.106439  0.004339
>> 4       5Y     1Y      0.1000  0.106609  0.006609
>> Average error: 0.0049
>> Model: Hull-White (numerical calibration)
>> Parameters: [ 0.0559314; 0.00609858 ]
>>   maturity length  volatility   implied     error
>> 0       1Y     5Y      0.1148  0.102941 -0.011859
>> 1       2Y     4Y      0.1108  0.105452 -0.005348
>> 2       3Y     3Y      0.1070  0.106560 -0.000440
>> 3       4Y     2Y      0.1021  0.107365  0.005265
>> 4       5Y     1Y      0.1000  0.108223  0.008223
>> Average error: 0.0062
>> Model: Black-Karasinski (numerical calibration)
>> Parameters: [ 0.0442393; 0.120656 ]
>>   maturity length  volatility   implied     error
>> 0       1Y     5Y      0.1148  0.103094 -0.011706
>> 1       2Y     4Y      0.1108  0.105637 -0.005163
>> 2       3Y     3Y      0.1070  0.106656 -0.000344
>> 3       4Y     2Y      0.1021  0.107333  0.005233
>> 4       5Y     1Y      0.1000  0.108028  0.008028
>> Average error: 0.0061
>>           model  in-the-money  at-the-money  out-of-the-money
>> 0   G2 analytic     42.919620     14.581452          3.443306
>> 1   HW analytic     42.245449     12.924016          2.512541
>> 2  HW numerical     42.340450     13.141040          2.614228
>> 3  BK numerical     41.805673     13.012682          3.271628
>>
>> --- GSR live setValue bump test ---
>> base rate: 0.04875825
>> base npv : 16.787915348295005
>>
>> setValue -> 0.04885825
>> Fatal Python error: Segmentation fault
>>
>> Current thread 0x00000001f0afd8c0 (most recent call first):
>>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>>
>> Current thread's C stack trace (most recent call first):
>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>   <truncated rest of calls>
>>
>> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
>> Segmentation fault: 11
>>
>>
>> _______________________________________________
>> QuantLib-users mailing list
>> Qua...@li...
>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>
>
> _______________________________________________
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>

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Aleksis A. R. <ale...@go...>]

Yep, ditching registerWith(stateProcess_); works fine as well and is the cleaner fix. 

thanks.

> On Apr 16, 2026, at 11:34, Peter Caspers <pca...@gm...> wrote:
> 
> Thanks. Since Gsr manages its stateProcess_, it does not need to and should not register with it as an Observer? I will take a closer look, but if you could test this change on your end, that would be valuable input.
> Best, Peter
> 
> 
> Aleksis Ali Raza via QuantLib-users <qua...@li... <mailto:qua...@li...>> schrieb am Do. 16. Apr. 2026 um 08:04:
>> Just to follow up on this, it's this line:
>> 
>> void Gsr::update() {
>>     if (stateProcess_ != nullptr) {
>>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();
>>     }
>>     LazyObject::update();
>> }
>> 
>> in the gsr class that seems to be the cause. in my case, commenting out that line out and rebuilding from source fixes the problem of my gsr-based swaption risk runs.
>> 
>> That line by itself looks pretty harmless but i think
>> registerWith(stateProcess_);
>> 
>> further down leads into some sort of feedback loop (setValue() →  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →  Gsr::update() →  …) and ultimately a seg fault.
>> 
>> 
>>> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go... <mailto:ale...@go...>> wrote:
>>> 
>>> Hi,
>>> 
>>> I’m encountering what appears to be a regression or stability issue in QuantLib 1.42 related to the GSR (Gaussian short rate) model and the observer framework.
>>> 
>>> Calling SimpleQuote.setValue() on a quote that is observed by a term structure used in a Gsr model causes a segmentation fault.
>>> 
>>> This occurs even in a minimal standalone script (no threading, no external frameworks).
>>> 
>>> Minimal reproduction
>>> 
>>> The following sequence is sufficient:
>>>         1.      Create a SimpleQuote
>>>         2.      Build a FlatForward curve using that quote
>>>         3.      Construct a Gsr model from that term structure
>>>         4.      Attach a Gaussian1dSwaptionEngine
>>>         5.      Call setValue() on the original SimpleQuote
>>> 
>>> This results in a segmentation fault. 
>>> 
>>> I have modified the Python/examples/bermudan_swaption.py file and attached it below to illustrate (along with error log below it).
>>> 
>>> Has something been changed in how one shud enquire / update quote objects that I missed? I am coming from 1.40, where this was not an issue. I am running python 3.14.4 on macOS 26.1.4 and using a source build of Quantlib/SWIG.
>>> 
>>> Thanks, Aleksis
>>> 
>>> # ---
>>> # jupyter:
>>> #   jupytext:
>>> #     formats: py:light
>>> #     text_representation:
>>> #       extension: .py
>>> #       format_name: light
>>> #       format_version: '1.5'
>>> #       jupytext_version: 1.4.2
>>> #   kernelspec:
>>> #     display_name: Python 3
>>> #     language: python
>>> #     name: python3
>>> # ---
>>> 
>>> # # Bermudan swaptions
>>> #
>>> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
>>> #
>>> # This file is part of QuantLib, a free-software/open-source library
>>> # for financial quantitative analysts and developers - https://www.quantlib.org/
>>> #
>>> # QuantLib is free software: you can redistribute it and/or modify it under the
>>> # terms of the QuantLib license.  You should have received a copy of the
>>> # license along with this program; if not, please email
>>> # <qua...@li... <mailto:qua...@li...>>. The license is also available online at
>>> # <https://www.quantlib.org/license.shtml>.
>>> #
>>> # This program is distributed in the hope that it will be useful, but WITHOUT
>>> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
>>> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>>> 
>>> import QuantLib as ql
>>> import pandas as pd
>>> 
>>> # ### Setup
>>> 
>>> todaysDate = ql.Date(15, ql.February, 2002)
>>> ql.Settings.instance().evaluationDate = todaysDate
>>> calendar = ql.TARGET()
>>> settlementDate = ql.Date(19, ql.February, 2002)
>>> 
>>> 
>>> def calibrate(model, helpers, l, name):
>>>     print("Model: %s" % name)
>>> 
>>>     method = ql.Simplex(l)
>>>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>>> 
>>>     print("Parameters: %s" % model.params())
>>> 
>>>     totalError = 0.0
>>>     data = []
>>>     for swaption, helper in zip(swaptionVols, helpers):
>>>         maturity, length, vol = swaption
>>>         NPV = helper.modelValue()
>>>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>>>         error = implied - vol
>>>         totalError += abs(error)
>>>         data.append((maturity, length, vol, implied, error))
>>>     averageError = totalError / len(helpers)
>>> 
>>>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>>> 
>>>     print("Average error: %.4f" % averageError)
>>> 
>>> 
>>> # ### Market data
>>> 
>>> swaptionVols = [
>>>     # maturity,          length,             volatility
>>>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>>>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>>>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>>>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>>>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
>>> ]
>>> 
>>> # This is a flat yield term structure implying a 1x5 swap at 5%.
>>> 
>>> rateQuote = ql.SimpleQuote(0.04875825)
>>> rate = ql.QuoteHandle(rateQuote)
>>> termStructure = ql.YieldTermStructureHandle(
>>>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
>>> )
>>> 
>>> # Define the ATM/OTM/ITM swaps:
>>> 
>>> swapEngine = ql.DiscountingSwapEngine(termStructure)
>>> 
>>> fixedLegFrequency = ql.Annual
>>> fixedLegTenor = ql.Period(1, ql.Years)
>>> fixedLegConvention = ql.Unadjusted
>>> floatingLegConvention = ql.ModifiedFollowing
>>> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
>>> floatingLegFrequency = ql.Semiannual
>>> floatingLegTenor = ql.Period(6, ql.Months)
>>> 
>>> payFixed = ql.Swap.Payer
>>> fixingDays = 2
>>> index = ql.Euribor6M(termStructure)
>>> floatingLegDayCounter = index.dayCounter()
>>> 
>>> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
>>> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>>> 
>>> fixedSchedule = ql.Schedule(
>>>     swapStart,
>>>     swapEnd,
>>>     fixedLegTenor,
>>>     calendar,
>>>     fixedLegConvention,
>>>     fixedLegConvention,
>>>     ql.DateGeneration.Forward,
>>>     False,
>>> )
>>> floatingSchedule = ql.Schedule(
>>>     swapStart,
>>>     swapEnd,
>>>     floatingLegTenor,
>>>     calendar,
>>>     floatingLegConvention,
>>>     floatingLegConvention,
>>>     ql.DateGeneration.Forward,
>>>     False,
>>> )
>>> 
>>> dummy = ql.VanillaSwap(
>>>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>> dummy.setPricingEngine(swapEngine)
>>> atmRate = dummy.fairRate()
>>> 
>>> atmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>> 
>>> otmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>> 
>>> itmSwap = ql.VanillaSwap(
>>>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>>>     floatingSchedule, index, 0.0, floatingLegDayCounter
>>> )
>>> 
>>> atmSwap.setPricingEngine(swapEngine)
>>> otmSwap.setPricingEngine(swapEngine)
>>> itmSwap.setPricingEngine(swapEngine)
>>> 
>>> helpers = [
>>>     ql.SwaptionHelper(
>>>         maturity,
>>>         length,
>>>         ql.makeQuoteHandle(vol),
>>>         index,
>>>         index.tenor(),
>>>         index.dayCounter(),
>>>         index.dayCounter(),
>>>         termStructure,
>>>     )
>>>     for maturity, length, vol in swaptionVols
>>> ]
>>> 
>>> times = {}
>>> for h in helpers:
>>>     for t in h.times():
>>>         times[t] = 1
>>> times = sorted(times.keys())
>>> 
>>> grid = ql.TimeGrid(times, 30)
>>> 
>>> G2model = ql.G2(termStructure)
>>> HWmodel = ql.HullWhite(termStructure)
>>> HWmodel2 = ql.HullWhite(termStructure)
>>> BKmodel = ql.BlackKarasinski(termStructure)
>>> 
>>> # ### Calibrations
>>> 
>>> for h in helpers:
>>>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
>>> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>>> 
>>> for h in helpers:
>>>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
>>> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>>> 
>>> for h in helpers:
>>>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
>>> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>>> 
>>> for h in helpers:
>>>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
>>> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>>> 
>>> 
>>> # ### Price Bermudan swaptions on defined swaps
>>> 
>>> bermudanDates = [d for d in fixedSchedule][:-1]
>>> exercise = ql.BermudanExercise(bermudanDates)
>>> 
>>> atmSwaption = ql.Swaption(atmSwap, exercise)
>>> otmSwaption = ql.Swaption(otmSwap, exercise)
>>> itmSwaption = ql.Swaption(itmSwap, exercise)
>>> 
>>> data = []
>>> 
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>>> 
>>> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>> 
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>>> 
>>> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>> 
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>>> 
>>> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>> 
>>> # +
>>> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>>> 
>>> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>>> # -
>>> 
>>> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>>> 
>>> # ### GSR live setValue bump test
>>> 
>>> import faulthandler
>>> faulthandler.enable(all_threads=True)
>>> 
>>> print("\n--- GSR live setValue bump test ---")
>>> 
>>> # Minimal European swaption test on the same underlying ATM swap
>>> euroExercise = ql.EuropeanExercise(swapStart)
>>> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>>> 
>>> # For a European GSR setup: no step dates, one sigma, one reversion
>>> stepDates = []
>>> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>>> 
>>> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
>>> gsrEngine = ql.Gaussian1dSwaptionEngine(
>>>     gsr,
>>>     64,     # integration points
>>>     7.0,    # std devs
>>>     True,
>>>     False,
>>>     termStructure
>>> )
>>> euroSwaption.setPricingEngine(gsrEngine)
>>> 
>>> base_rate = 0.04875825
>>> 
>>> print("base rate:", base_rate)
>>> print("base npv :", euroSwaption.NPV())
>>> 
>>> # Single bump up/down
>>> for bump in [1e-4, -1e-4, 0.0]:
>>>     bumped = base_rate + bump
>>>     print(f"\nsetValue -> {bumped:.8f}")
>>>     rateQuote.setValue(bumped)
>>>     print("npv =", euroSwaption.NPV())
>>> 
>>> # Stress loop: repeatedly bump the observed quote and reprice
>>> print("\nstarting stress loop...")
>>> for i in range(200):
>>>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>>>     rateQuote.setValue(bumped)
>>>     npv = euroSwaption.NPV()
>>>     if i % 20 == 0:
>>>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>>> 
>>> # Restore
>>> rateQuote.setValue(base_rate)
>>> print("\nrestored rate:", base_rate)
>>> print("restored npv :", euroSwaption.NPV())
>>> 
>>> Log:
>>> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
>>> Model: G2 (analytic formulae)
>>> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.101974 -0.012826
>>> 1       2Y     4Y      0.1108  0.105611 -0.005189
>>> 2       3Y     3Y      0.1070  0.106950 -0.000050
>>> 3       4Y     2Y      0.1021  0.107873  0.005773
>>> 4       5Y     1Y      0.1000  0.108556  0.008556
>>> Average error: 0.0065
>>> Model: Hull-White (analytic formulae)
>>> Parameters: [ 0.046379; 0.00586848 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.106223 -0.008577
>>> 1       2Y     4Y      0.1108  0.106292 -0.004508
>>> 2       3Y     3Y      0.1070  0.106338 -0.000662
>>> 3       4Y     2Y      0.1021  0.106439  0.004339
>>> 4       5Y     1Y      0.1000  0.106609  0.006609
>>> Average error: 0.0049
>>> Model: Hull-White (numerical calibration)
>>> Parameters: [ 0.0559314; 0.00609858 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.102941 -0.011859
>>> 1       2Y     4Y      0.1108  0.105452 -0.005348
>>> 2       3Y     3Y      0.1070  0.106560 -0.000440
>>> 3       4Y     2Y      0.1021  0.107365  0.005265
>>> 4       5Y     1Y      0.1000  0.108223  0.008223
>>> Average error: 0.0062
>>> Model: Black-Karasinski (numerical calibration)
>>> Parameters: [ 0.0442393; 0.120656 ]
>>>   maturity length  volatility   implied     error
>>> 0       1Y     5Y      0.1148  0.103094 -0.011706
>>> 1       2Y     4Y      0.1108  0.105637 -0.005163
>>> 2       3Y     3Y      0.1070  0.106656 -0.000344
>>> 3       4Y     2Y      0.1021  0.107333  0.005233
>>> 4       5Y     1Y      0.1000  0.108028  0.008028
>>> Average error: 0.0061
>>>           model  in-the-money  at-the-money  out-of-the-money
>>> 0   G2 analytic     42.919620     14.581452          3.443306
>>> 1   HW analytic     42.245449     12.924016          2.512541
>>> 2  HW numerical     42.340450     13.141040          2.614228
>>> 3  BK numerical     41.805673     13.012682          3.271628
>>> 
>>> --- GSR live setValue bump test ---
>>> base rate: 0.04875825
>>> base npv : 16.787915348295005
>>> 
>>> setValue -> 0.04885825
>>> Fatal Python error: Segmentation fault
>>> 
>>> Current thread 0x00000001f0afd8c0 (most recent call first):
>>>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>>>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>>> 
>>> Current thread's C stack trace (most recent call first):
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>>>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>>>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>>>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>>>   <truncated rest of calls>
>>> 
>>> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
>>> Segmentation fault: 11
>> 
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>> QuantLib-users mailing list
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Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Peter C. <pca...@gm...>]

Thanks. Since Gsr manages its stateProcess_, it does not need to and should
not register with it as an Observer? I will take a closer look, but if you
could test this change on your end, that would be valuable input.
Best, Peter


Aleksis Ali Raza via QuantLib-users <qua...@li...>
schrieb am Do. 16. Apr. 2026 um 08:04:

> Just to follow up on this, it's this line:
>
> void Gsr::update() {
>     if (stateProcess_ != nullptr) {
>         ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
>
> *ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();*
>     }
>     LazyObject::update();
> }
>
> in the gsr class that seems to be the cause. in my case, commenting out
> that line out and rebuilding from source fixes the problem of my gsr-based
> swaption risk runs.
>
> That line by itself looks pretty harmless but i think
>
> registerWith(stateProcess_);
>
> further down leads into some sort of feedback loop (setValue() →
>  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →
>  Gsr::update() →  …) and ultimately a seg fault.
>
> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go...>
> wrote:
>
> Hi,
>
> I’m encountering what appears to be a regression or stability issue in
> QuantLib 1.42 related to the GSR (Gaussian short rate) model and the
> observer framework.
>
> Calling SimpleQuote.setValue() on a quote that is observed by a term
> structure used in a Gsr model causes a segmentation fault.
>
> This occurs even in a minimal standalone script (no threading, no external
> frameworks).
>
> Minimal reproduction
>
> The following sequence is sufficient:
>         1.      Create a SimpleQuote
>         2.      Build a FlatForward curve using that quote
>         3.      Construct a Gsr model from that term structure
>         4.      Attach a Gaussian1dSwaptionEngine
>         5.      Call setValue() on the original SimpleQuote
>
> This results in a segmentation fault.
>
> I have modified the Python/examples/bermudan_swaption.py file and attached
> it below to illustrate (along with error log below it).
>
> Has something been changed in how one shud enquire / update quote objects
> that I missed? I am coming from 1.40, where this was not an issue. I am
> running python 3.14.4 on macOS 26.1.4 and using a source build of
> Quantlib/SWIG.
>
> Thanks, Aleksis
>
> # ---
> # jupyter:
> #   jupytext:
> #     formats: py:light
> #     text_representation:
> #       extension: .py
> #       format_name: light
> #       format_version: '1.5'
> #       jupytext_version: 1.4.2
> #   kernelspec:
> #     display_name: Python 3
> #     language: python
> #     name: python3
> # ---
>
> # # Bermudan swaptions
> #
> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
> #
> # This file is part of QuantLib, a free-software/open-source library
> # for financial quantitative analysts and developers - https://www.quantlib.org/
> #
> # QuantLib is free software: you can redistribute it and/or modify it under the
> # terms of the QuantLib license.  You should have received a copy of the
> # license along with this program; if not, please email
> # <qua...@li...>. The license is also available online at
> # <https://www.quantlib.org/license.shtml>.
> #
> # This program is distributed in the hope that it will be useful, but WITHOUT
> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
> # FOR A PARTICULAR PURPOSE.  See the license for more details.
>
> import QuantLib as ql
> import pandas as pd
>
> # ### Setup
>
> todaysDate = ql.Date(15, ql.February, 2002)
> ql.Settings.instance().evaluationDate = todaysDate
> calendar = ql.TARGET()
> settlementDate = ql.Date(19, ql.February, 2002)
>
>
> def calibrate(model, helpers, l, name):
>     print("Model: %s" % name)
>
>     method = ql.Simplex(l)
>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
>
>     print("Parameters: %s" % model.params())
>
>     totalError = 0.0
>     data = []
>     for swaption, helper in zip(swaptionVols, helpers):
>         maturity, length, vol = swaption
>         NPV = helper.modelValue()
>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>         error = implied - vol
>         totalError += abs(error)
>         data.append((maturity, length, vol, implied, error))
>     averageError = totalError / len(helpers)
>
>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
>
>     print("Average error: %.4f" % averageError)
>
>
> # ### Market data
>
> swaptionVols = [
>     # maturity,          length,             volatility
>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
> ]
>
> # This is a flat yield term structure implying a 1x5 swap at 5%.
>
> rateQuote = ql.SimpleQuote(0.04875825)
> rate = ql.QuoteHandle(rateQuote)
> termStructure = ql.YieldTermStructureHandle(
>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
> )
>
> # Define the ATM/OTM/ITM swaps:
>
> swapEngine = ql.DiscountingSwapEngine(termStructure)
>
> fixedLegFrequency = ql.Annual
> fixedLegTenor = ql.Period(1, ql.Years)
> fixedLegConvention = ql.Unadjusted
> floatingLegConvention = ql.ModifiedFollowing
> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
> floatingLegFrequency = ql.Semiannual
> floatingLegTenor = ql.Period(6, ql.Months)
>
> payFixed = ql.Swap.Payer
> fixingDays = 2
> index = ql.Euribor6M(termStructure)
> floatingLegDayCounter = index.dayCounter()
>
> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
>
> fixedSchedule = ql.Schedule(
>     swapStart,
>     swapEnd,
>     fixedLegTenor,
>     calendar,
>     fixedLegConvention,
>     fixedLegConvention,
>     ql.DateGeneration.Forward,
>     False,
> )
> floatingSchedule = ql.Schedule(
>     swapStart,
>     swapEnd,
>     floatingLegTenor,
>     calendar,
>     floatingLegConvention,
>     floatingLegConvention,
>     ql.DateGeneration.Forward,
>     False,
> )
>
> dummy = ql.VanillaSwap(
>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
> )
> dummy.setPricingEngine(swapEngine)
> atmRate = dummy.fairRate()
>
> atmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
>
> otmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
>
> itmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
>
> atmSwap.setPricingEngine(swapEngine)
> otmSwap.setPricingEngine(swapEngine)
> itmSwap.setPricingEngine(swapEngine)
>
> helpers = [
>     ql.SwaptionHelper(
>         maturity,
>         length,
>         ql.makeQuoteHandle(vol),
>         index,
>         index.tenor(),
>         index.dayCounter(),
>         index.dayCounter(),
>         termStructure,
>     )
>     for maturity, length, vol in swaptionVols
> ]
>
> times = {}
> for h in helpers:
>     for t in h.times():
>         times[t] = 1
> times = sorted(times.keys())
>
> grid = ql.TimeGrid(times, 30)
>
> G2model = ql.G2(termStructure)
> HWmodel = ql.HullWhite(termStructure)
> HWmodel2 = ql.HullWhite(termStructure)
> BKmodel = ql.BlackKarasinski(termStructure)
>
> # ### Calibrations
>
> for h in helpers:
>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
>
> for h in helpers:
>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
>
> for h in helpers:
>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
>
> for h in helpers:
>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
>
>
> # ### Price Bermudan swaptions on defined swaps
>
> bermudanDates = [d for d in fixedSchedule][:-1]
> exercise = ql.BermudanExercise(bermudanDates)
>
> atmSwaption = ql.Swaption(atmSwap, exercise)
> otmSwaption = ql.Swaption(otmSwap, exercise)
> itmSwaption = ql.Swaption(itmSwap, exercise)
>
> data = []
>
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
>
> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
>
> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
>
> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
>
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
>
> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
> # -
>
> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
>
> # ### GSR live setValue bump test
>
> import faulthandler
> faulthandler.enable(all_threads=True)
>
> print("\n--- GSR live setValue bump test ---")
>
> # Minimal European swaption test on the same underlying ATM swap
> euroExercise = ql.EuropeanExercise(swapStart)
> euroSwaption = ql.Swaption(atmSwap, euroExercise)
>
> # For a European GSR setup: no step dates, one sigma, one reversion
> stepDates = []
> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
>
> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
> gsrEngine = ql.Gaussian1dSwaptionEngine(
>     gsr,
>     64,     # integration points
>     7.0,    # std devs
>     True,
>     False,
>     termStructure
> )
> euroSwaption.setPricingEngine(gsrEngine)
>
> base_rate = 0.04875825
>
> print("base rate:", base_rate)
> print("base npv :", euroSwaption.NPV())
>
> # Single bump up/down
> for bump in [1e-4, -1e-4, 0.0]:
>     bumped = base_rate + bump
>     print(f"\nsetValue -> {bumped:.8f}")
>     rateQuote.setValue(bumped)
>     print("npv =", euroSwaption.NPV())
>
> # Stress loop: repeatedly bump the observed quote and reprice
> print("\nstarting stress loop...")
> for i in range(200):
>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>     rateQuote.setValue(bumped)
>     npv = euroSwaption.NPV()
>     if i % 20 == 0:
>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
>
> # Restore
> rateQuote.setValue(base_rate)
> print("\nrestored rate:", base_rate)
> print("restored npv :", euroSwaption.NPV())
>
>
> Log:
>
> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
> Model: G2 (analytic formulae)
> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.101974 -0.012826
> 1       2Y     4Y      0.1108  0.105611 -0.005189
> 2       3Y     3Y      0.1070  0.106950 -0.000050
> 3       4Y     2Y      0.1021  0.107873  0.005773
> 4       5Y     1Y      0.1000  0.108556  0.008556
> Average error: 0.0065
> Model: Hull-White (analytic formulae)
> Parameters: [ 0.046379; 0.00586848 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.106223 -0.008577
> 1       2Y     4Y      0.1108  0.106292 -0.004508
> 2       3Y     3Y      0.1070  0.106338 -0.000662
> 3       4Y     2Y      0.1021  0.106439  0.004339
> 4       5Y     1Y      0.1000  0.106609  0.006609
> Average error: 0.0049
> Model: Hull-White (numerical calibration)
> Parameters: [ 0.0559314; 0.00609858 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.102941 -0.011859
> 1       2Y     4Y      0.1108  0.105452 -0.005348
> 2       3Y     3Y      0.1070  0.106560 -0.000440
> 3       4Y     2Y      0.1021  0.107365  0.005265
> 4       5Y     1Y      0.1000  0.108223  0.008223
> Average error: 0.0062
> Model: Black-Karasinski (numerical calibration)
> Parameters: [ 0.0442393; 0.120656 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.103094 -0.011706
> 1       2Y     4Y      0.1108  0.105637 -0.005163
> 2       3Y     3Y      0.1070  0.106656 -0.000344
> 3       4Y     2Y      0.1021  0.107333  0.005233
> 4       5Y     1Y      0.1000  0.108028  0.008028
> Average error: 0.0061
>           model  in-the-money  at-the-money  out-of-the-money
> 0   G2 analytic     42.919620     14.581452          3.443306
> 1   HW analytic     42.245449     12.924016          2.512541
> 2  HW numerical     42.340450     13.141040          2.614228
> 3  BK numerical     41.805673     13.012682          3.271628
>
> --- GSR live setValue bump test ---
> base rate: 0.04875825
> base npv : 16.787915348295005
>
> setValue -> 0.04885825
> Fatal Python error: Segmentation fault
>
> Current thread 0x00000001f0afd8c0 (most recent call first):
>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
>
> Current thread's C stack trace (most recent call first):
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   <truncated rest of calls>
>
> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
> Segmentation fault: 11
>
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Aleksis A. R. <ale...@go...>]

Just to follow up on this, it's this line:

void Gsr::update() {
    if (stateProcess_ != nullptr) {
        ext::static_pointer_cast<GsrProcess>(stateProcess_)->flushCache();
        ext::static_pointer_cast<GsrProcess>(stateProcess_)->notifyObservers();
    }
    LazyObject::update();
}

in the gsr class that seems to be the cause. in my case, commenting out that line out and rebuilding from source fixes the problem of my gsr-based swaption risk runs.

That line by itself looks pretty harmless but i think
registerWith(stateProcess_);

further down leads into some sort of feedback loop (setValue() →  notifyObservers() →  Gsr::update() →  stateProcess_->notifyObservers() →  Gsr::update() →  …) and ultimately a seg fault.


> On Apr 16, 2026, at 09:13, Aleksis Ali Raza <ale...@go...> wrote:
> 
> Hi,
> 
> I’m encountering what appears to be a regression or stability issue in QuantLib 1.42 related to the GSR (Gaussian short rate) model and the observer framework.
> 
> Calling SimpleQuote.setValue() on a quote that is observed by a term structure used in a Gsr model causes a segmentation fault.
> 
> This occurs even in a minimal standalone script (no threading, no external frameworks).
> 
> Minimal reproduction
> 
> The following sequence is sufficient:
>         1.      Create a SimpleQuote
>         2.      Build a FlatForward curve using that quote
>         3.      Construct a Gsr model from that term structure
>         4.      Attach a Gaussian1dSwaptionEngine
>         5.      Call setValue() on the original SimpleQuote
> 
> This results in a segmentation fault. 
> 
> I have modified the Python/examples/bermudan_swaption.py file and attached it below to illustrate (along with error log below it).
> 
> Has something been changed in how one shud enquire / update quote objects that I missed? I am coming from 1.40, where this was not an issue. I am running python 3.14.4 on macOS 26.1.4 and using a source build of Quantlib/SWIG.
> 
> Thanks, Aleksis
> 
> # ---
> # jupyter:
> #   jupytext:
> #     formats: py:light
> #     text_representation:
> #       extension: .py
> #       format_name: light
> #       format_version: '1.5'
> #       jupytext_version: 1.4.2
> #   kernelspec:
> #     display_name: Python 3
> #     language: python
> #     name: python3
> # ---
> 
> # # Bermudan swaptions
> #
> # Copyright (&copy;) 2004, 2005, 2006, 2007 StatPro Italia srl
> #
> # This file is part of QuantLib, a free-software/open-source library
> # for financial quantitative analysts and developers - https://www.quantlib.org/
> #
> # QuantLib is free software: you can redistribute it and/or modify it under the
> # terms of the QuantLib license.  You should have received a copy of the
> # license along with this program; if not, please email
> # <qua...@li...>. The license is also available online at
> # <https://www.quantlib.org/license.shtml>.
> #
> # This program is distributed in the hope that it will be useful, but WITHOUT
> # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
> # FOR A PARTICULAR PURPOSE.  See the license for more details.
> 
> import QuantLib as ql
> import pandas as pd
> 
> # ### Setup
> 
> todaysDate = ql.Date(15, ql.February, 2002)
> ql.Settings.instance().evaluationDate = todaysDate
> calendar = ql.TARGET()
> settlementDate = ql.Date(19, ql.February, 2002)
> 
> 
> def calibrate(model, helpers, l, name):
>     print("Model: %s" % name)
> 
>     method = ql.Simplex(l)
>     model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))
> 
>     print("Parameters: %s" % model.params())
> 
>     totalError = 0.0
>     data = []
>     for swaption, helper in zip(swaptionVols, helpers):
>         maturity, length, vol = swaption
>         NPV = helper.modelValue()
>         implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
>         error = implied - vol
>         totalError += abs(error)
>         data.append((maturity, length, vol, implied, error))
>     averageError = totalError / len(helpers)
> 
>     print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))
> 
>     print("Average error: %.4f" % averageError)
> 
> 
> # ### Market data
> 
> swaptionVols = [
>     # maturity,          length,             volatility
>     (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
>     (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
>     (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
>     (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
>     (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
> ]
> 
> # This is a flat yield term structure implying a 1x5 swap at 5%.
> 
> rateQuote = ql.SimpleQuote(0.04875825)
> rate = ql.QuoteHandle(rateQuote)
> termStructure = ql.YieldTermStructureHandle(
>     ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
> )
> 
> # Define the ATM/OTM/ITM swaps:
> 
> swapEngine = ql.DiscountingSwapEngine(termStructure)
> 
> fixedLegFrequency = ql.Annual
> fixedLegTenor = ql.Period(1, ql.Years)
> fixedLegConvention = ql.Unadjusted
> floatingLegConvention = ql.ModifiedFollowing
> fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
> floatingLegFrequency = ql.Semiannual
> floatingLegTenor = ql.Period(6, ql.Months)
> 
> payFixed = ql.Swap.Payer
> fixingDays = 2
> index = ql.Euribor6M(termStructure)
> floatingLegDayCounter = index.dayCounter()
> 
> swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
> swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)
> 
> fixedSchedule = ql.Schedule(
>     swapStart,
>     swapEnd,
>     fixedLegTenor,
>     calendar,
>     fixedLegConvention,
>     fixedLegConvention,
>     ql.DateGeneration.Forward,
>     False,
> )
> floatingSchedule = ql.Schedule(
>     swapStart,
>     swapEnd,
>     floatingLegTenor,
>     calendar,
>     floatingLegConvention,
>     floatingLegConvention,
>     ql.DateGeneration.Forward,
>     False,
> )
> 
> dummy = ql.VanillaSwap(
>     payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
> )
> dummy.setPricingEngine(swapEngine)
> atmRate = dummy.fairRate()
> 
> atmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
> 
> otmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
> 
> itmSwap = ql.VanillaSwap(
>     payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
>     floatingSchedule, index, 0.0, floatingLegDayCounter
> )
> 
> atmSwap.setPricingEngine(swapEngine)
> otmSwap.setPricingEngine(swapEngine)
> itmSwap.setPricingEngine(swapEngine)
> 
> helpers = [
>     ql.SwaptionHelper(
>         maturity,
>         length,
>         ql.makeQuoteHandle(vol),
>         index,
>         index.tenor(),
>         index.dayCounter(),
>         index.dayCounter(),
>         termStructure,
>     )
>     for maturity, length, vol in swaptionVols
> ]
> 
> times = {}
> for h in helpers:
>     for t in h.times():
>         times[t] = 1
> times = sorted(times.keys())
> 
> grid = ql.TimeGrid(times, 30)
> 
> G2model = ql.G2(termStructure)
> HWmodel = ql.HullWhite(termStructure)
> HWmodel2 = ql.HullWhite(termStructure)
> BKmodel = ql.BlackKarasinski(termStructure)
> 
> # ### Calibrations
> 
> for h in helpers:
>     h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
> calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")
> 
> for h in helpers:
>     h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
> calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")
> 
> for h in helpers:
>     h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
> calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")
> 
> for h in helpers:
>     h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
> calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")
> 
> 
> # ### Price Bermudan swaptions on defined swaps
> 
> bermudanDates = [d for d in fixedSchedule][:-1]
> exercise = ql.BermudanExercise(bermudanDates)
> 
> atmSwaption = ql.Swaption(atmSwap, exercise)
> otmSwaption = ql.Swaption(otmSwap, exercise)
> itmSwaption = ql.Swaption(itmSwap, exercise)
> 
> data = []
> 
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
> 
> data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
> 
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
> 
> data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
> 
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
> 
> data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
> 
> # +
> atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
> otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
> itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
> 
> data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
> # -
> 
> print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))
> 
> # ### GSR live setValue bump test
> 
> import faulthandler
> faulthandler.enable(all_threads=True)
> 
> print("\n--- GSR live setValue bump test ---")
> 
> # Minimal European swaption test on the same underlying ATM swap
> euroExercise = ql.EuropeanExercise(swapStart)
> euroSwaption = ql.Swaption(atmSwap, euroExercise)
> 
> # For a European GSR setup: no step dates, one sigma, one reversion
> stepDates = []
> sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
> reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
> 
> gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
> gsrEngine = ql.Gaussian1dSwaptionEngine(
>     gsr,
>     64,     # integration points
>     7.0,    # std devs
>     True,
>     False,
>     termStructure
> )
> euroSwaption.setPricingEngine(gsrEngine)
> 
> base_rate = 0.04875825
> 
> print("base rate:", base_rate)
> print("base npv :", euroSwaption.NPV())
> 
> # Single bump up/down
> for bump in [1e-4, -1e-4, 0.0]:
>     bumped = base_rate + bump
>     print(f"\nsetValue -> {bumped:.8f}")
>     rateQuote.setValue(bumped)
>     print("npv =", euroSwaption.NPV())
> 
> # Stress loop: repeatedly bump the observed quote and reprice
> print("\nstarting stress loop...")
> for i in range(200):
>     bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
>     rateQuote.setValue(bumped)
>     npv = euroSwaption.NPV()
>     if i % 20 == 0:
>         print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")
> 
> # Restore
> rateQuote.setValue(base_rate)
> print("\nrestored rate:", base_rate)
> print("restored npv :", euroSwaption.NPV())
> 
> Log:
> aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
> Model: G2 (analytic formulae)
> Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.101974 -0.012826
> 1       2Y     4Y      0.1108  0.105611 -0.005189
> 2       3Y     3Y      0.1070  0.106950 -0.000050
> 3       4Y     2Y      0.1021  0.107873  0.005773
> 4       5Y     1Y      0.1000  0.108556  0.008556
> Average error: 0.0065
> Model: Hull-White (analytic formulae)
> Parameters: [ 0.046379; 0.00586848 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.106223 -0.008577
> 1       2Y     4Y      0.1108  0.106292 -0.004508
> 2       3Y     3Y      0.1070  0.106338 -0.000662
> 3       4Y     2Y      0.1021  0.106439  0.004339
> 4       5Y     1Y      0.1000  0.106609  0.006609
> Average error: 0.0049
> Model: Hull-White (numerical calibration)
> Parameters: [ 0.0559314; 0.00609858 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.102941 -0.011859
> 1       2Y     4Y      0.1108  0.105452 -0.005348
> 2       3Y     3Y      0.1070  0.106560 -0.000440
> 3       4Y     2Y      0.1021  0.107365  0.005265
> 4       5Y     1Y      0.1000  0.108223  0.008223
> Average error: 0.0062
> Model: Black-Karasinski (numerical calibration)
> Parameters: [ 0.0442393; 0.120656 ]
>   maturity length  volatility   implied     error
> 0       1Y     5Y      0.1148  0.103094 -0.011706
> 1       2Y     4Y      0.1108  0.105637 -0.005163
> 2       3Y     3Y      0.1070  0.106656 -0.000344
> 3       4Y     2Y      0.1021  0.107333  0.005233
> 4       5Y     1Y      0.1000  0.108028  0.008028
> Average error: 0.0061
>           model  in-the-money  at-the-money  out-of-the-money
> 0   G2 analytic     42.919620     14.581452          3.443306
> 1   HW analytic     42.245449     12.924016          2.512541
> 2  HW numerical     42.340450     13.141040          2.614228
> 3  BK numerical     41.805673     13.012682          3.271628
> 
> --- GSR live setValue bump test ---
> base rate: 0.04875825
> base npv : 16.787915348295005
> 
> setValue -> 0.04885825
> Fatal Python error: Segmentation fault
> 
> Current thread 0x00000001f0afd8c0 (most recent call first):
>   File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
>   File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>
> 
> Current thread's C stack trace (most recent call first):
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
>   Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
>   Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
>   Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
>   <truncated rest of calls>
> 
> Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
> Segmentation fault: 11

[Quantlib-users] Quantlib Python 1.42 segmentation fault in GSR model when calling SimpleQuote.setValue()

[Aleksis A. R. <ale...@go...>]

Hi,

I’m encountering what appears to be a regression or stability issue in QuantLib 1.42 related to the GSR (Gaussian short rate) model and the observer framework.

Calling SimpleQuote.setValue() on a quote that is observed by a term structure used in a Gsr model causes a segmentation fault.

This occurs even in a minimal standalone script (no threading, no external frameworks).

Minimal reproduction

The following sequence is sufficient:
        1.      Create a SimpleQuote
        2.      Build a FlatForward curve using that quote
        3.      Construct a Gsr model from that term structure
        4.      Attach a Gaussian1dSwaptionEngine
        5.      Call setValue() on the original SimpleQuote

This results in a segmentation fault. 

I have modified the Python/examples/bermudan_swaption.py file and attached it below to illustrate (along with error log below it).

Has something been changed in how one shud enquire / update quote objects that I missed? I am coming from 1.40, where this was not an issue. I am running python 3.14.4 on macOS 26.1.4 and using a source build of Quantlib/SWIG.

Thanks, Aleksis

# ---
# jupyter:
#   jupytext:
#     formats: py:light
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.4.2
#   kernelspec:
#     display_name: Python 3
#     language: python
#     name: python3
# ---

# # Bermudan swaptions
#
# Copyright (©) 2004, 2005, 2006, 2007 StatPro Italia srl
#
# This file is part of QuantLib, a free-software/open-source library
# for financial quantitative analysts and developers - https://www.quantlib.org/
#
# QuantLib is free software: you can redistribute it and/or modify it under the
# terms of the QuantLib license.  You should have received a copy of the
# license along with this program; if not, please email
# <qua...@li...>. The license is also available online at
# <https://www.quantlib.org/license.shtml>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE.  See the license for more details.

import QuantLib as ql
import pandas as pd

# ### Setup

todaysDate = ql.Date(15, ql.February, 2002)
ql.Settings.instance().evaluationDate = todaysDate
calendar = ql.TARGET()
settlementDate = ql.Date(19, ql.February, 2002)


def calibrate(model, helpers, l, name):
    print("Model: %s" % name)

    method = ql.Simplex(l)
    model.calibrate(helpers, method, ql.EndCriteria(1000, 250, 1e-7, 1e-7, 1e-7))

    print("Parameters: %s" % model.params())

    totalError = 0.0
    data = []
    for swaption, helper in zip(swaptionVols, helpers):
        maturity, length, vol = swaption
        NPV = helper.modelValue()
        implied = helper.impliedVolatility(NPV, 1.0e-4, 1000, 0.05, 0.50)
        error = implied - vol
        totalError += abs(error)
        data.append((maturity, length, vol, implied, error))
    averageError = totalError / len(helpers)

    print(pd.DataFrame(data, columns=["maturity", "length", "volatility", "implied", "error"]))

    print("Average error: %.4f" % averageError)


# ### Market data

swaptionVols = [
    # maturity,          length,             volatility
    (ql.Period(1, ql.Years), ql.Period(5, ql.Years), 0.1148),
    (ql.Period(2, ql.Years), ql.Period(4, ql.Years), 0.1108),
    (ql.Period(3, ql.Years), ql.Period(3, ql.Years), 0.1070),
    (ql.Period(4, ql.Years), ql.Period(2, ql.Years), 0.1021),
    (ql.Period(5, ql.Years), ql.Period(1, ql.Years), 0.1000),
]

# This is a flat yield term structure implying a 1x5 swap at 5%.

rateQuote = ql.SimpleQuote(0.04875825)
rate = ql.QuoteHandle(rateQuote)
termStructure = ql.YieldTermStructureHandle(
    ql.FlatForward(settlementDate, rate, ql.Actual365Fixed())
)

# Define the ATM/OTM/ITM swaps:

swapEngine = ql.DiscountingSwapEngine(termStructure)

fixedLegFrequency = ql.Annual
fixedLegTenor = ql.Period(1, ql.Years)
fixedLegConvention = ql.Unadjusted
floatingLegConvention = ql.ModifiedFollowing
fixedLegDayCounter = ql.Thirty360(ql.Thirty360.European)
floatingLegFrequency = ql.Semiannual
floatingLegTenor = ql.Period(6, ql.Months)

payFixed = ql.Swap.Payer
fixingDays = 2
index = ql.Euribor6M(termStructure)
floatingLegDayCounter = index.dayCounter()

swapStart = calendar.advance(settlementDate, 1, ql.Years, floatingLegConvention)
swapEnd = calendar.advance(swapStart, 5, ql.Years, floatingLegConvention)

fixedSchedule = ql.Schedule(
    swapStart,
    swapEnd,
    fixedLegTenor,
    calendar,
    fixedLegConvention,
    fixedLegConvention,
    ql.DateGeneration.Forward,
    False,
)
floatingSchedule = ql.Schedule(
    swapStart,
    swapEnd,
    floatingLegTenor,
    calendar,
    floatingLegConvention,
    floatingLegConvention,
    ql.DateGeneration.Forward,
    False,
)

dummy = ql.VanillaSwap(
    payFixed, 100.0, fixedSchedule, 0.0, fixedLegDayCounter, floatingSchedule, index, 0.0, floatingLegDayCounter
)
dummy.setPricingEngine(swapEngine)
atmRate = dummy.fairRate()

atmSwap = ql.VanillaSwap(
    payFixed, 1000.0, fixedSchedule, atmRate, fixedLegDayCounter,
    floatingSchedule, index, 0.0, floatingLegDayCounter
)

otmSwap = ql.VanillaSwap(
    payFixed, 1000.0, fixedSchedule, atmRate * 1.2, fixedLegDayCounter,
    floatingSchedule, index, 0.0, floatingLegDayCounter
)

itmSwap = ql.VanillaSwap(
    payFixed, 1000.0, fixedSchedule, atmRate * 0.8, fixedLegDayCounter,
    floatingSchedule, index, 0.0, floatingLegDayCounter
)

atmSwap.setPricingEngine(swapEngine)
otmSwap.setPricingEngine(swapEngine)
itmSwap.setPricingEngine(swapEngine)

helpers = [
    ql.SwaptionHelper(
        maturity,
        length,
        ql.makeQuoteHandle(vol),
        index,
        index.tenor(),
        index.dayCounter(),
        index.dayCounter(),
        termStructure,
    )
    for maturity, length, vol in swaptionVols
]

times = {}
for h in helpers:
    for t in h.times():
        times[t] = 1
times = sorted(times.keys())

grid = ql.TimeGrid(times, 30)

G2model = ql.G2(termStructure)
HWmodel = ql.HullWhite(termStructure)
HWmodel2 = ql.HullWhite(termStructure)
BKmodel = ql.BlackKarasinski(termStructure)

# ### Calibrations

for h in helpers:
    h.setPricingEngine(ql.G2SwaptionEngine(G2model, 6.0, 16))
calibrate(G2model, helpers, 0.05, "G2 (analytic formulae)")

for h in helpers:
    h.setPricingEngine(ql.JamshidianSwaptionEngine(HWmodel))
calibrate(HWmodel, helpers, 0.05, "Hull-White (analytic formulae)")

for h in helpers:
    h.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, grid))
calibrate(HWmodel2, helpers, 0.05, "Hull-White (numerical calibration)")

for h in helpers:
    h.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, grid))
calibrate(BKmodel, helpers, 0.05, "Black-Karasinski (numerical calibration)")


# ### Price Bermudan swaptions on defined swaps

bermudanDates = [d for d in fixedSchedule][:-1]
exercise = ql.BermudanExercise(bermudanDates)

atmSwaption = ql.Swaption(atmSwap, exercise)
otmSwaption = ql.Swaption(otmSwap, exercise)
itmSwaption = ql.Swaption(itmSwap, exercise)

data = []

# +
atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))
itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(G2model, 50))

data.append(("G2 analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))

# +
atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))
itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel, 50))

data.append(("HW analytic", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))

# +
atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))
itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(HWmodel2, 50))

data.append(("HW numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))

# +
atmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
otmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))
itmSwaption.setPricingEngine(ql.TreeSwaptionEngine(BKmodel, 50))

data.append(("BK numerical", itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV()))
# -

print(pd.DataFrame(data, columns=["model", "in-the-money", "at-the-money", "out-of-the-money"]))

# ### GSR live setValue bump test

import faulthandler
faulthandler.enable(all_threads=True)

print("\n--- GSR live setValue bump test ---")

# Minimal European swaption test on the same underlying ATM swap
euroExercise = ql.EuropeanExercise(swapStart)
euroSwaption = ql.Swaption(atmSwap, euroExercise)

# For a European GSR setup: no step dates, one sigma, one reversion
stepDates = []
sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]
reversions = [ql.QuoteHandle(ql.SimpleQuote(0.01))]

gsr = ql.Gsr(termStructure, stepDates, sigmas, reversions)
gsrEngine = ql.Gaussian1dSwaptionEngine(
    gsr,
    64,     # integration points
    7.0,    # std devs
    True,
    False,
    termStructure
)
euroSwaption.setPricingEngine(gsrEngine)

base_rate = 0.04875825

print("base rate:", base_rate)
print("base npv :", euroSwaption.NPV())

# Single bump up/down
for bump in [1e-4, -1e-4, 0.0]:
    bumped = base_rate + bump
    print(f"\nsetValue -> {bumped:.8f}")
    rateQuote.setValue(bumped)
    print("npv =", euroSwaption.NPV())

# Stress loop: repeatedly bump the observed quote and reprice
print("\nstarting stress loop...")
for i in range(200):
    bumped = base_rate + (1e-4 if i % 2 == 0 else -1e-4)
    rateQuote.setValue(bumped)
    npv = euroSwaption.NPV()
    if i % 20 == 0:
        print(f"iter {i:03d}  rate={bumped:.8f}  npv={npv}")

# Restore
rateQuote.setValue(base_rate)
print("\nrestored rate:", base_rate)
print("restored npv :", euroSwaption.NPV())

Log:
aleksisaliraza@Alis-MacBook-Pro:~/QuantLib-SWIG-1.42/Python/examples$ python3.14 bermudan-swaption.py
Model: G2 (analytic formulae)
Parameters: [ 0.0765369; 0.00279407; 0.0619638; 0.00954892; -1 ]
  maturity length  volatility   implied     error
0       1Y     5Y      0.1148  0.101974 -0.012826
1       2Y     4Y      0.1108  0.105611 -0.005189
2       3Y     3Y      0.1070  0.106950 -0.000050
3       4Y     2Y      0.1021  0.107873  0.005773
4       5Y     1Y      0.1000  0.108556  0.008556
Average error: 0.0065
Model: Hull-White (analytic formulae)
Parameters: [ 0.046379; 0.00586848 ]
  maturity length  volatility   implied     error
0       1Y     5Y      0.1148  0.106223 -0.008577
1       2Y     4Y      0.1108  0.106292 -0.004508
2       3Y     3Y      0.1070  0.106338 -0.000662
3       4Y     2Y      0.1021  0.106439  0.004339
4       5Y     1Y      0.1000  0.106609  0.006609
Average error: 0.0049
Model: Hull-White (numerical calibration)
Parameters: [ 0.0559314; 0.00609858 ]
  maturity length  volatility   implied     error
0       1Y     5Y      0.1148  0.102941 -0.011859
1       2Y     4Y      0.1108  0.105452 -0.005348
2       3Y     3Y      0.1070  0.106560 -0.000440
3       4Y     2Y      0.1021  0.107365  0.005265
4       5Y     1Y      0.1000  0.108223  0.008223
Average error: 0.0062
Model: Black-Karasinski (numerical calibration)
Parameters: [ 0.0442393; 0.120656 ]
  maturity length  volatility   implied     error
0       1Y     5Y      0.1148  0.103094 -0.011706
1       2Y     4Y      0.1108  0.105637 -0.005163
2       3Y     3Y      0.1070  0.106656 -0.000344
3       4Y     2Y      0.1021  0.107333  0.005233
4       5Y     1Y      0.1000  0.108028  0.008028
Average error: 0.0061
          model  in-the-money  at-the-money  out-of-the-money
0   G2 analytic     42.919620     14.581452          3.443306
1   HW analytic     42.245449     12.924016          2.512541
2  HW numerical     42.340450     13.141040          2.614228
3  BK numerical     41.805673     13.012682          3.271628

--- GSR live setValue bump test ---
base rate: 0.04875825
base npv : 16.787915348295005

setValue -> 0.04885825
Fatal Python error: Segmentation fault

Current thread 0x00000001f0afd8c0 (most recent call first):
  File "/opt/homebrew/lib/python3.14/site-packages/QuantLib/QuantLib.py", line 6180 in setValue
  File "/Users/aleksisaliraza/QuantLib-SWIG-1.42/Python/examples/bermudan-swaption.py", line 277 in <module>

Current thread's C stack trace (most recent call first):
  Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at _Py_DumpStack+0x44 [0x1036d79d8]
  Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_dump_c_stack+0x58 [0x1036e9f0c]
  Binary file "/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Python", at faulthandler_fatal_error+0x140 [0x1036e9dd0]
  Binary file "/usr/lib/system/libsystem_platform.dylib", at _sigtramp+0x38 [0x184cfd7a4]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib10Observable15notifyObserversEv+0x80 [0x10df9c4ec]
  Binary file "/usr/local/lib/libQuantLib.0.dylib", at _ZN8QuantLib3Gsr6updateEv+0xb4 [0x10df754d8]
  <truncated rest of calls>

Extension modules: QuantLib._QuantLib, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._mt19937, numpy.random._generator, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing (total: 52)
Segmentation fault: 11

[Quantlib-users] QuantLib 1.42 released

[Luigi B. <lui...@gm...>]

QuantLib 1.42 is now available for download at <
https://www.quantlib.org/download.shtml>; precompiled binaries are also
available from PyPI and NuGet for Python and C# respectively.

The list of changes for this release is at <
https://github.com/lballabio/QuantLib/releases/tag/v1.42>.

If you have any problems with this release, please report them here on the
QuantLib mailing list (<qua...@li...>) or open a
GitHub issue at <https://github.com/lballabio/quantlib/issues>.

Re: [Quantlib-users] Release candidate for version 1.42

[Dirk E. <ed...@de...>]

On 13 April 2026 at 16:14, Luigi Ballabio wrote:
| Ok, I see the problem — apt is still giving you SWIG 4.4.0: see <
| https://buildd.debian.org/status/package.php?p=swig&suite=sid>.  And quite
| simply, 4.4.0 had a bug.

Ahh. Easy enough. That explains it.
 
| I would have thought sid had 4.4.1 at this point, given that it was
| released last December.  Upgrading to that SWIG version would fix the
| build.  If you can get hold of the maintainer, maybe you can nudge them?

I will file a courtesy bug report gentlu nudging the maintainer(s). [ Some
time passes. ]  Done, see #1133688.

Dirk

-- 
Dirk Eddelbuettel | ed...@de... | http://dirk.eddelbuettel.com

Re: [Quantlib-users] Release candidate for version 1.42

[Luigi B. <lui...@gm...>]

Ok, I see the problem — apt is still giving you SWIG 4.4.0: see <
https://buildd.debian.org/status/package.php?p=swig&suite=sid>.  And quite
simply, 4.4.0 had a bug.

I would have thought sid had 4.4.1 at this point, given that it was
released last December.  Upgrading to that SWIG version would fix the
build.  If you can get hold of the maintainer, maybe you can nudge them?

Luigi


On Mon, Apr 13, 2026 at 3:27 PM Dirk Eddelbuettel <ed...@de...> wrote:

>
> Hi Luigi,
>
> On 13 April 2026 at 14:54, Luigi Ballabio wrote:
> | Hi Dirk, as usual thanks for the support.
> |
> | > I did once again have to carry the 'make Python 3.10 the minimum' patch
> | forward as I had for the last two (?)  cycles.
> |
> | Meaning that you tried not applying it and the build failed?
>
> Yes. There were really two problems here.  First, once a patch is placed
> into
> debian/patches/ in the sources and referenced in debian/patches/series, it
> is
> always applied (using a system called `quilt`, which AFAIK is a general
> tool
> from outside Debian). Usually that 'just works' as the sources do not
> change. Here it failed, I believe the last and this time.  So with the
> patch
> NOT applied the build then failed.
>
> And am I guessing correctly that there even was/is the intent to automate
> this as there are Python/setup.py and Python/setup.py.in and I may well be
> doing something wrong if I have to patch both?
>
> | I thought it was no longer necessary if Debian picked up SWIG 4.4.1 (the
> upgrade to
> | 4.4.0 made it necessary, 4.4.1 should have fixed it).
>
> I would not know -- you are the swig expert here.
>
> | > I presume you keep it 3.8 for a reason?
> |
> | Well, since it doesn't require us to create extra wheels (the ones we
> | create with the stable ABI work with all versions) it's supposed to be
> | zero-cost and possibly make some people happier.  But if it still
> requires
> | you to patch the build, I'll probably drop it next time.
>
> Can setup.py somehow pick up how/where it builds and could we condition on
> that?  There must be a way to support both 'very conservative Python 3.8'
> for
> manywheels and all that as well as our (and presumably other people's) use
> on
> newer systems that do not go back as far?  In my case 'manually' adjusting
> is
> not a big deal.  If we could make the patch auto-apply it would be even
> easier.  Could setup.py 'read a file' we patch (and that does not change)
> or
> 'listen to an env var' (that I could set) ?
>
> Dirk
>
> --
> Dirk Eddelbuettel | ed...@de... | http://dirk.eddelbuettel.com
>

Re: [Quantlib-users] Release candidate for version 1.42

[Dirk E. <ed...@de...>]

Hi Luigi,

On 13 April 2026 at 14:54, Luigi Ballabio wrote:
| Hi Dirk, as usual thanks for the support.
| 
| > I did once again have to carry the 'make Python 3.10 the minimum' patch
| forward as I had for the last two (?)  cycles.
| 
| Meaning that you tried not applying it and the build failed?

Yes. There were really two problems here.  First, once a patch is placed into
debian/patches/ in the sources and referenced in debian/patches/series, it is
always applied (using a system called `quilt`, which AFAIK is a general tool
from outside Debian). Usually that 'just works' as the sources do not
change. Here it failed, I believe the last and this time.  So with the patch
NOT applied the build then failed.

And am I guessing correctly that there even was/is the intent to automate
this as there are Python/setup.py and Python/setup.py.in and I may well be
doing something wrong if I have to patch both?

| I thought it was no longer necessary if Debian picked up SWIG 4.4.1 (the upgrade to
| 4.4.0 made it necessary, 4.4.1 should have fixed it).

I would not know -- you are the swig expert here.
 
| > I presume you keep it 3.8 for a reason?
| 
| Well, since it doesn't require us to create extra wheels (the ones we
| create with the stable ABI work with all versions) it's supposed to be
| zero-cost and possibly make some people happier.  But if it still requires
| you to patch the build, I'll probably drop it next time.

Can setup.py somehow pick up how/where it builds and could we condition on
that?  There must be a way to support both 'very conservative Python 3.8' for
manywheels and all that as well as our (and presumably other people's) use on
newer systems that do not go back as far?  In my case 'manually' adjusting is
not a big deal.  If we could make the patch auto-apply it would be even
easier.  Could setup.py 'read a file' we patch (and that does not change) or
'listen to an env var' (that I could set) ?

Dirk

-- 
Dirk Eddelbuettel | ed...@de... | http://dirk.eddelbuettel.com

Re: [Quantlib-users] Release candidate for version 1.42

[Luigi B. <lui...@gm...>]

Hi Dirk, as usual thanks for the support.

> I did once again have to carry the 'make Python 3.10 the minimum' patch
forward as I had for the last two (?)  cycles.

Meaning that you tried not applying it and the build failed? I thought it
was no longer necessary if Debian picked up SWIG 4.4.1 (the upgrade to
4.4.0 made it necessary, 4.4.1 should have fixed it).

> I presume you keep it 3.8 for a reason?

Well, since it doesn't require us to create extra wheels (the ones we
create with the stable ABI work with all versions) it's supposed to be
zero-cost and possibly make some people happier.  But if it still requires
you to patch the build, I'll probably drop it next time.

Luigi



On Fri, Apr 10, 2026 at 3:17 PM Dirk Eddelbuettel <ed...@de...> wrote:

>
> On 2 April 2026 at 12:29, Luigi Ballabio wrote:
> | Hi all,
> |    a release candidate for QuantLib 1.42 is available at <
> | https://github.com/lballabio/QuantLib/releases/tag/v1.42-rc>.  Python
> | wheels are also available at <
> | https://test.pypi.org/project/QuantLib/1.42rc0/>, and a C# NuGet
> package is
> | at <https://int.nugettest.org/packages/QuantLib/1.42.0-rc>.  If you have
> | some time, please try it out and report any issues on GitHub (or here on
> | the mailing list).  Thanks!
>
> All good from here on the Debian side. Built (and uploaded) QuantLib
> yesterday, followed up with QuantLib-SWIG (for just Python) now. I did once
> again have to carry the 'make Python 3.10 the minimum' patch forward as I
> had
> for the last two (?)  cycles.
>
> I presume you keep it 3.8 for a reason?  If not a gentle nudge to adjust :)
> The patch basically is still the same as
>
>
> https://sources.debian.org/src/quantlib-swig/1.41-1/debian/patches/upgrade-minimum-python-version-3.10.patch
>
> but will get slightly changed line numbers when '1.41.99' (as we cannot
> append '-rc' in our version sorting) pops up in due course.
>
> Cheers,  Dirk
>
> --
> dirk.eddelbuettel.com | @eddelbuettel | ed...@de...
>
> Sponsor me for Tour de Shore 2026! Donate at
>    https://www.pledgereg.com/536435
> More about the ride at
>
> https://dirk.eddelbuettel.com/blog/2026/04/03#sponsor_tour_de_shore_2026
>
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Release candidate for version 1.42

[Dirk E. <ed...@de...>]

On 2 April 2026 at 12:29, Luigi Ballabio wrote:
| Hi all,
|    a release candidate for QuantLib 1.42 is available at <
| https://github.com/lballabio/QuantLib/releases/tag/v1.42-rc>.  Python
| wheels are also available at <
| https://test.pypi.org/project/QuantLib/1.42rc0/>, and a C# NuGet package is
| at <https://int.nugettest.org/packages/QuantLib/1.42.0-rc>.  If you have
| some time, please try it out and report any issues on GitHub (or here on
| the mailing list).  Thanks!

All good from here on the Debian side. Built (and uploaded) QuantLib
yesterday, followed up with QuantLib-SWIG (for just Python) now. I did once
again have to carry the 'make Python 3.10 the minimum' patch forward as I had
for the last two (?)  cycles.

I presume you keep it 3.8 for a reason?  If not a gentle nudge to adjust :)
The patch basically is still the same as

   https://sources.debian.org/src/quantlib-swig/1.41-1/debian/patches/upgrade-minimum-python-version-3.10.patch

but will get slightly changed line numbers when '1.41.99' (as we cannot
append '-rc' in our version sorting) pops up in due course.

Cheers,  Dirk

-- 
dirk.eddelbuettel.com | @eddelbuettel | ed...@de...

Sponsor me for Tour de Shore 2026! Donate at
   https://www.pledgereg.com/536435 
More about the ride at
   https://dirk.eddelbuettel.com/blog/2026/04/03#sponsor_tour_de_shore_2026

[Quantlib-users] Release candidate for version 1.42

[Luigi B. <lui...@gm...>]

Hi all,
   a release candidate for QuantLib 1.42 is available at <
https://github.com/lballabio/QuantLib/releases/tag/v1.42-rc>.  Python
wheels are also available at <
https://test.pypi.org/project/QuantLib/1.42rc0/>, and a C# NuGet package is
at <https://int.nugettest.org/packages/QuantLib/1.42.0-rc>.  If you have
some time, please try it out and report any issues on GitHub (or here on
the mailing list).  Thanks!

Luigi

Re: [Quantlib-users] Error in writing Python tests for FXForward

[Luigi B. <lui...@gm...>]

Hello Chirag,
    please don't use the latest 1.41.  Backporting your class manually
might skip some steps.  Use a checkout from the latest master from GitHub,
where your FxForward is already included correctly.  Use a checkout for the
SWIG part too; you'll have to do that anyway in order to open a pull
request.

Luigi


On Mon, Mar 16, 2026 at 8:53 AM Chirag Desai <chi...@ya...>
wrote:

> Hi Luigi
>
> I am very sorry but may not have been clear enough
>
> 1) I got the latest versions of Quantlib 1.41 and Quantlib-SWIG 1.41
> 2) I manually added the FXForward class in my local version and it builds
> fine and the test for it under testsuite works fine
> 3) I added the fxforward.i and amended ql.i
> 4) When I build the python wheel it cannot find the FX Forward class
>
> (When I do the above exercise without the FX Forward class, I can build
> the wheel successfully and all works well)
>
> [image: Inline image]
>
> On Sunday, March 15, 2026 at 12:33:03 AM GMT+8, Luigi Ballabio <
> lui...@gm...> wrote:
>
>
> Your new class is not in QuantLib 1.41.  You need to check out the latest
> versions of QuantLib and QuantLib-SWIG from GitHub and use those ones.
>
> Luigi
>
>
> On Sat, Mar 14, 2026 at 5:41 AM Chirag Desai <chi...@ya...>
> wrote:
>
> Thank you Mr Luigi
>
> 1) I added in ql.i and recompiled Quantlib as well
>
> 2) I get error messages now when building the wheel stating FXForward
> class cannot be found. I have attached the error messages.
>
> Build was successful
> [image: Inline image]
>
> [image: Inline image]
>
>
>
> On Friday, March 13, 2026 at 10:50:47 PM GMT+8, Luigi Ballabio <
> lui...@gm...> wrote:
>
>
> Hi Chirag, did you add your fxforward.i to the files included in ql.i ?
>
> Luigi
>
>
> On Fri, Mar 13, 2026 at 7:42 AM Chirag Desai <chi...@ya...>
> wrote:
>
> Hi All
> I built Quantlib with the new fxforward.hpp, fxforward.cpp ,
> fxforwarddiscountingengine hpp.cpp which I had recently contributed and
> successfully in my own version of Quantlib 1.41. All tests work
>
> I placed my fxforward.i file and swig runs successfully
>
> The wheel is generated successfully
>
> I am able to pip install the wheel and successfully can “import Quantlib
> as ql” and also can run certain Python files under SWIG in Examples and
> Tests (european-option.py, test_blackformula.py)
>
> For some reason my FX Forward tests all fail. (attached test_fxforward.py)
>
> How do I check if my FXForward class was successfully exposed to the
> Python API ? If any tools available ?
>
> When I do ql.  there is no dropdown either in IDE like VSCode to see what
> ql functions are being exposed in Python ?
> Thank you all
>
>
>
>
>
>

Re: [Quantlib-users] Error in writing Python tests for FXForward

[Chirag D. <chi...@ya...>]

 Hi Luigi
I am very sorry but may not have been clear enough
1) I got the latest versions of Quantlib 1.41 and Quantlib-SWIG 1.412) I manually added the FXForward class in my local version and it builds fine and the test for it under testsuite works fine3) I added the fxforward.i and amended ql.i4) When I build the python wheel it cannot find the FX Forward class
(When I do the above exercise without the FX Forward class, I can build the wheel successfully and all works well)


    On Sunday, March 15, 2026 at 12:33:03 AM GMT+8, Luigi Ballabio <lui...@gm...> wrote:  
 
 Your new class is not in QuantLib 1.41.  You need to check out the latest versions of QuantLib and QuantLib-SWIG from GitHub and use those ones.
Luigi

On Sat, Mar 14, 2026 at 5:41 AM Chirag Desai <chi...@ya...> wrote:

 Thank you Mr Luigi
1) I added in ql.i and recompiled Quantlib as well
2) I get error messages now when building the wheel stating FXForward class cannot be found. I have attached the error messages.
Build was successful 





    On Friday, March 13, 2026 at 10:50:47 PM GMT+8, Luigi Ballabio <lui...@gm...> wrote:  
 
 Hi Chirag, did you add your fxforward.i to the files included in ql.i ?
Luigi

On Fri, Mar 13, 2026 at 7:42 AM Chirag Desai <chi...@ya...> wrote:

 Hi AllI built Quantlib with the newfxforward.hpp, fxforward.cpp , fxforwarddiscountingengine hpp.cpp which I had recently contributed and successfullyin my own version of Quantlib 1.41. All tests work


I placed my fxforward.i file andswig runs successfully



The wheel is generated successfully



I am able to pip install the wheeland successfully can “import Quantlib as ql” and also can run certain Pythonfiles under SWIG in Examples and Tests (european-option.py,test_blackformula.py)

For some reason my FX Forward testsall fail. (attached test_fxforward.py)

How do I check if my FXForward classwas successfully exposed to the Python API ? If any tools available ?

When I do ql.  there is no dropdown either in IDE like VSCodeto see what ql functions are being exposed in Python ?
Thank you all
 




  
  

Re: [Quantlib-users] Error in writing Python tests for FXForward

[Luigi B. <lui...@gm...>]

Your new class is not in QuantLib 1.41.  You need to check out the latest
versions of QuantLib and QuantLib-SWIG from GitHub and use those ones.

Luigi


On Sat, Mar 14, 2026 at 5:41 AM Chirag Desai <chi...@ya...>
wrote:

> Thank you Mr Luigi
>
> 1) I added in ql.i and recompiled Quantlib as well
>
> 2) I get error messages now when building the wheel stating FXForward
> class cannot be found. I have attached the error messages.
>
> Build was successful
> [image: Inline image]
>
> [image: Inline image]
>
>
>
> On Friday, March 13, 2026 at 10:50:47 PM GMT+8, Luigi Ballabio <
> lui...@gm...> wrote:
>
>
> Hi Chirag, did you add your fxforward.i to the files included in ql.i ?
>
> Luigi
>
>
> On Fri, Mar 13, 2026 at 7:42 AM Chirag Desai <chi...@ya...>
> wrote:
>
> Hi All
> I built Quantlib with the new fxforward.hpp, fxforward.cpp ,
> fxforwarddiscountingengine hpp.cpp which I had recently contributed and
> successfully in my own version of Quantlib 1.41. All tests work
>
> I placed my fxforward.i file and swig runs successfully
>
> The wheel is generated successfully
>
> I am able to pip install the wheel and successfully can “import Quantlib
> as ql” and also can run certain Python files under SWIG in Examples and
> Tests (european-option.py, test_blackformula.py)
>
> For some reason my FX Forward tests all fail. (attached test_fxforward.py)
>
> How do I check if my FXForward class was successfully exposed to the
> Python API ? If any tools available ?
>
> When I do ql.  there is no dropdown either in IDE like VSCode to see what
> ql functions are being exposed in Python ?
> Thank you all
>
>
>
>
>
>

Re: [Quantlib-users] Error in writing Python tests for FXForward

[Chirag D. <chi...@ya...>]

 Thank you Mr Luigi
1) I added in ql.i and recompiled Quantlib as well
2) I get error messages now when building the wheel stating FXForward class cannot be found. I have attached the error messages.
Build was successful 





    On Friday, March 13, 2026 at 10:50:47 PM GMT+8, Luigi Ballabio <lui...@gm...> wrote:  
 
 Hi Chirag, did you add your fxforward.i to the files included in ql.i ?
Luigi

On Fri, Mar 13, 2026 at 7:42 AM Chirag Desai <chi...@ya...> wrote:

 Hi AllI built Quantlib with the newfxforward.hpp, fxforward.cpp , fxforwarddiscountingengine hpp.cpp which I had recently contributed and successfullyin my own version of Quantlib 1.41. All tests work


I placed my fxforward.i file andswig runs successfully



The wheel is generated successfully



I am able to pip install the wheeland successfully can “import Quantlib as ql” and also can run certain Pythonfiles under SWIG in Examples and Tests (european-option.py,test_blackformula.py)

For some reason my FX Forward testsall fail. (attached test_fxforward.py)

How do I check if my FXForward classwas successfully exposed to the Python API ? If any tools available ?

When I do ql.  there is no dropdown either in IDE like VSCodeto see what ql functions are being exposed in Python ?
Thank you all
 




  

Re: [Quantlib-users] Error in writing Python tests for FXForward

[Luigi B. <lui...@gm...>]

Hi Chirag, did you add your fxforward.i to the files included in ql.i ?

Luigi


On Fri, Mar 13, 2026 at 7:42 AM Chirag Desai <chi...@ya...>
wrote:

> Hi All
> I built Quantlib with the new fxforward.hpp, fxforward.cpp ,
> fxforwarddiscountingengine hpp.cpp which I had recently contributed and
> successfully in my own version of Quantlib 1.41. All tests work
>
> I placed my fxforward.i file and swig runs successfully
>
> The wheel is generated successfully
>
> I am able to pip install the wheel and successfully can “import Quantlib
> as ql” and also can run certain Python files under SWIG in Examples and
> Tests (european-option.py, test_blackformula.py)
>
> For some reason my FX Forward tests all fail. (attached test_fxforward.py)
>
> How do I check if my FXForward class was successfully exposed to the
> Python API ? If any tools available ?
>
> When I do ql.  there is no dropdown either in IDE like VSCode to see what
> ql functions are being exposed in Python ?
>
> Thank you all
>
>
>
>
>

[Quantlib-users] Error in writing Python tests for FXForward

[Chirag D. <chi...@ya...>]

 Hi AllI built Quantlib with the newfxforward.hpp, fxforward.cpp , fxforwarddiscountingengine hpp.cpp which I had recently contributed and successfullyin my own version of Quantlib 1.41. All tests work


I placed my fxforward.i file andswig runs successfully



The wheel is generated successfully



I am able to pip install the wheeland successfully can “import Quantlib as ql” and also can run certain Pythonfiles under SWIG in Examples and Tests (european-option.py,test_blackformula.py)

For some reason my FX Forward testsall fail. (attached test_fxforward.py)

How do I check if my FXForward classwas successfully exposed to the Python API ? If any tools available ?

When I do ql.  there is no dropdown either in IDE like VSCodeto see what ql functions are being exposed in Python ?

Thank you all