quantlib-users — QuantLib users discussion list

[Quantlib-users] end_of_month question

[jian Xu <jia...@gm...>]

Hi,
I'm confused by the "endOfMonth" setting.  The document says:
```
endOfMonth : If the start date is at the end of the month, whether
other dates are required to be scheduled at the end of the month
(except the last date).
```

So my understanding is that if the start date is NOT the end of the
month, then this setting doesn't matter. (is this correct?)

However, it does matter in the following example.  As the input
suggests, I expect the first date in the schedule should be 2018-03-29
(Thursday, NOT end of the month).  However, I only get this result
when I set endOfMonth=False.  If I set it to true, the first date in
the schedule will be 2018-03-31 (Saturday).  Why?

Thanks a lot.

import QuantLib as ql
calendar = ql.UnitedStates(ql.UnitedStates.GovernmentBond)
schedule = ql.MakeSchedule(effectiveDate=ql.Date(29,3,2018),
                        terminationDate=ql.Date(29, 3, 2021),
                        tenor=ql.Period("3M"),
                        calendar=calendar,
                        convention=ql.Unadjusted,
                        terminalDateConvention=ql.Unadjusted,
                        rule=ql.DateGeneration.Forward,
                        endOfMonth=True,
                        firstDate=ql.Date(29, 6, 2018),
                        nextToLastDate=ql.Date())

print(schedule[0])

Re: [Quantlib-users] exotic non linear payoff

[Jonathan S. <sw...@gm...>]

Hi Mohammad,

There's a test suite for pricing various kinds of averaging (Asian) options
under different engines that you can use as a reference.

https://github.com/lballabio/QuantLib/blob/master/test-suite/asianoptions.cpp

Not sure whether the available payoff types can fully capture the
expression that you require. If not, then you may have to extend one of the
payoff types and pass it into the instrument constructor instead of what
you see in the test suite.

Jonathan


On Sat, Feb 26, 2022 at 1:09 AM Mohammad Shoja-talab <msh...@gm...>
wrote:

> Hi All,
>
> I'd like to price a payoff which looks like this: max( S - C * S/Avg(S), 0)
> Where S is the spot price, Avg(S) is average of spot price in specified
> (future) fixing dates and C is a constant. How best can I do that using
> quantlib?
>
> Thanks
>
> --
> Mo Shoja
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

[Quantlib-users] exotic non linear payoff

[Mohammad Shoja-t. <msh...@gm...>]

Hi All,

I'd like to price a payoff which looks like this: max( S - C * S/Avg(S), 0)
Where S is the spot price, Avg(S) is average of spot price in specified
(future) fixing dates and C is a constant. How best can I do that using
quantlib?

Thanks

-- 
Mo Shoja

Re: [Quantlib-users] Constructing Smile Curve from Quoted Vols in QuantLib

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

In addition, you might take a look at

https://github.com/OpenSourceRisk/Engine/blob/master/QuantExt/qle/termstructures/blackvolsurfacebfrr.cpp

which is not QuantLib, but uses Dimitri's DeltaVolQuote and
BlackDeltaCalcuator at its heart to construct a vol smile from 10 and 25 BF
/ RR quotes, so there might be some inspiration there for you.

Best Regards
Peter

On Wed, 16 Feb 2022 at 14:41, Luigi Ballabio <lui...@gm...>
wrote:

> Hello Goutham,
>     I'm not very familiar with that part of the code, but I think you can
> do something similar to what's done in the vanna-volga barrier engine:
>
>
> https://github.com/lballabio/QuantLib/blob/master/ql/experimental/barrieroption/vannavolgabarrierengine.cpp#L97-L130
>
> First, it uses the BlackDeltaCalculator class to calculate the strike
> corresponding to the quotes (lines 97-113); then it creates an
> interpolation between them (117-127; it uses vanna-volga interpolation, but
> I guess you can choose another one if you want); then it extracts from the
> interpolation the volatility corresponding to the option strike.
>
> Hope this helps,
>     Luigi
>
>
>
> On Wed, Feb 16, 2022 at 10:28 AM Goutham Mahesh <
> gou...@gm...> wrote:
>
>> Hi all,
>>
>> I am pricing FX options and was going through the Quantlib Python Guide
>> to assist me.
>> I came to this section on DeltaVolQuotes:
>>
>> deltaType = ql.DeltaVolQuote.Fwd # Also supports: Spot, PaSpot, PaFwd
>> atmType = ql.DeltaVolQuote.AtmFwd # Also supports: AtmSpot,
>> AtmDeltaNeutral, AtmVegaMax, AtmGammaMax,␣
>> ˓→AtmPutCall50
>> maturity = 1.0
>> volAtm, vol25DeltaCall, vol25DeltaPut = 0.08, 0.075, 0.095
>> atmDeltaQuote = ql.DeltaVolQuote(ql.QuoteHandle(ql.SimpleQuote(volAtm)),
>> deltaType, maturity, atmType)
>> vol25DeltaPutQuote = ql.DeltaVolQuote(-0.25,
>> ql.QuoteHandle(ql.SimpleQuote(vol25DeltaPut)), maturity,␣
>> ˓→deltaType)
>> vol25DeltaCallQuote = ql.DeltaVolQuote(0.25,
>> ql.QuoteHandle(ql.SimpleQuote(vol25DeltaCall)), maturity,␣
>> ˓→deltaType)
>>
>> I have the ATMf and 25DRR and 25D Fly quotes for various tenors as
>> attached in the table below. My doubt is using the code above, is there any
>> way I can construct the volatility smile curve using QuantLib to derive the
>> implied volatility of an option.
>>
>> Vol Table:
>> ATMF Vol 25D RR 25D Std 10D RR 10 Std
>> Bid Ask
>> 1m 6.1 6.45 0.25 0.5 0.05 0.15 0.45 0.9 0.16 0.48
>> 2m 6.05 6.4 0.3 0.55 0.1 0.25 0.54 0.99 0.32 0.8
>> 3m 6.05 6.3 0.35 0.6 0.15 0.35 0.63 1.08 0.48 1.12
>> 6m 5.9 6.1 0.45 0.65 0.2 0.35 0.81 1.17 0.64 1.12
>> 9m 5.9 6.1 0.45 0.7 0.2 0.35 0.81 1.26 0.64 1.12
>> 1y 5.9 6.1 0.5 0.7 0.2 0.4 0.9 1.26 0.64 1.28
>> 2y 5.5 6 0.3 0.7 0.25 0.55 0.54 1.26 0.8 1.76
>> 3y 5.2 5.7 0.3 0.7 0.3 0.7 0.54 1.26 0.96 2.24
>> 4y 5.1 5.6 0.1 0.7 0.35 0.85 0.18 1.26 1.12 2.72
>> 5y 5 5.5 -0.1 0.4 0.5 0.9 -0.18 0.72 1.6 2.88
>>
>> Thank You,
>> Best,
>> Goutham
>> _______________________________________________
>> 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
>

[Quantlib-users] calculating option price with PDE solver

[Yevgeniy G. <yev...@la...>]

Hi,

we're trying to use QuantLib PDE solver to calculate option prices (calls and puts).  Our final code will include vol adjustment based on time, but for the first version we're trying to implement a call option price with a constant vol.

The results I'm getting don't match the expected: the red line shows intrinsic value of the option (y) depending on percent strike (x).  The blue line is calculated by PDE.

[cid:image001.png@01D82358.969B6450]

Can you help finding the error?

Here's the code:

from typing import List
import QuantLib as ql
import numpy as np
import pylab as pl

class DiffusionOperator:
    def __init__(self, t_max: float, x_values: List[float], cur_vals_t: List[float]):
        self.__mesher = ql.FdmMesherComposite( ql.Predefined1dMesher(x_values))
        self.__t_max = t_max
        self.__n = len(x_values)
        self.__x_values = ql.Array(x_values)
        self.__cur_vals_t = ql.Array(cur_vals_t)
        self.__dx = ql.FirstDerivativeOp(0, self.__mesher)
        self.__ddx = ql.SecondDerivativeOp(0, self.__mesher)
        self.__map = ql.TripleBandLinearOp(0, self.__mesher)

    def size(self) -> int:
        return 1

    def setTime(self, t1: float, t2: float):
        self.__map.axpyb( ql.Array(1, 1.0), self.__dx, self.__ddx.mult(self.__cur_vals_t), ql.Array())

    def apply(self, r: ql.Array) -> ql.Array:
        return self.__map.apply(r)

    def solve_splitting(self, direction: int, r: ql.Array, dt: float) -> ql.Array:
        return self.__map.solve_splitting(r, dt, 1.0)

if __name__ == "__main__":
    n_points = 500; n_time_steps = 800; x_min = 0.1; x_max = 4; t_max = 0.2; bs_vol = 0.1;
    x_values = np.arange(x_min, x_max + 1e-8, (x_max - x_min) / (n_points - 1))
    cur_values_t = 0.5 * bs_vol**2 * x_values*x_values
    y0 = np.maximum(0, 1 - x_values)
    y_values = ql.Array( list(y0.copy()) )
    op = DiffusionOperator(t_max, list(x_values), list(cur_values_t))
    mesher = ql.FdmMesherComposite(ql.Predefined1dMesher(x_values))
    solver = ql.FdmBackwardSolver(
        ql.FdmLinearOpCompositeProxy(op),
        ql.FdmBoundaryConditionSet(
            [ql.FdmDirichletBoundary(mesher, 1, 0, ql.FdmBoundaryCondition.Lower),
            ql.FdmDirichletBoundary(mesher, 0, 0, ql.FdmBoundaryCondition.Upper)]  ),
        ql.FdmStepConditionComposite(ql.DoubleVector(), ql.FdmStepConditionVector()),
        ql.FdmSchemeDesc.CrankNicolson()
    )
    solver.rollback(y_values, t_max, 0.0, n_time_steps, 0)
    pl.plot(x_values, y_values, '.')
    pl.plot(x_values, y0, 'r-')
    pl.show()





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Re: [Quantlib-users] Constructing Smile Curve from Quoted Vols in QuantLib

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

Hello Goutham,
    I'm not very familiar with that part of the code, but I think you can
do something similar to what's done in the vanna-volga barrier engine:

https://github.com/lballabio/QuantLib/blob/master/ql/experimental/barrieroption/vannavolgabarrierengine.cpp#L97-L130

First, it uses the BlackDeltaCalculator class to calculate the strike
corresponding to the quotes (lines 97-113); then it creates an
interpolation between them (117-127; it uses vanna-volga interpolation, but
I guess you can choose another one if you want); then it extracts from the
interpolation the volatility corresponding to the option strike.

Hope this helps,
    Luigi



On Wed, Feb 16, 2022 at 10:28 AM Goutham Mahesh <gou...@gm...>
wrote:

> Hi all,
>
> I am pricing FX options and was going through the Quantlib Python Guide to
> assist me.
> I came to this section on DeltaVolQuotes:
>
> deltaType = ql.DeltaVolQuote.Fwd # Also supports: Spot, PaSpot, PaFwd
> atmType = ql.DeltaVolQuote.AtmFwd # Also supports: AtmSpot,
> AtmDeltaNeutral, AtmVegaMax, AtmGammaMax,␣
> ˓→AtmPutCall50
> maturity = 1.0
> volAtm, vol25DeltaCall, vol25DeltaPut = 0.08, 0.075, 0.095
> atmDeltaQuote = ql.DeltaVolQuote(ql.QuoteHandle(ql.SimpleQuote(volAtm)),
> deltaType, maturity, atmType)
> vol25DeltaPutQuote = ql.DeltaVolQuote(-0.25,
> ql.QuoteHandle(ql.SimpleQuote(vol25DeltaPut)), maturity,␣
> ˓→deltaType)
> vol25DeltaCallQuote = ql.DeltaVolQuote(0.25,
> ql.QuoteHandle(ql.SimpleQuote(vol25DeltaCall)), maturity,␣
> ˓→deltaType)
>
> I have the ATMf and 25DRR and 25D Fly quotes for various tenors as
> attached in the table below. My doubt is using the code above, is there any
> way I can construct the volatility smile curve using QuantLib to derive the
> implied volatility of an option.
>
> Vol Table:
> ATMF Vol 25D RR 25D Std 10D RR 10 Std
> Bid Ask
> 1m 6.1 6.45 0.25 0.5 0.05 0.15 0.45 0.9 0.16 0.48
> 2m 6.05 6.4 0.3 0.55 0.1 0.25 0.54 0.99 0.32 0.8
> 3m 6.05 6.3 0.35 0.6 0.15 0.35 0.63 1.08 0.48 1.12
> 6m 5.9 6.1 0.45 0.65 0.2 0.35 0.81 1.17 0.64 1.12
> 9m 5.9 6.1 0.45 0.7 0.2 0.35 0.81 1.26 0.64 1.12
> 1y 5.9 6.1 0.5 0.7 0.2 0.4 0.9 1.26 0.64 1.28
> 2y 5.5 6 0.3 0.7 0.25 0.55 0.54 1.26 0.8 1.76
> 3y 5.2 5.7 0.3 0.7 0.3 0.7 0.54 1.26 0.96 2.24
> 4y 5.1 5.6 0.1 0.7 0.35 0.85 0.18 1.26 1.12 2.72
> 5y 5 5.5 -0.1 0.4 0.5 0.9 -0.18 0.72 1.6 2.88
>
> Thank You,
> Best,
> Goutham
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

[Quantlib-users] Constructing Smile Curve from Quoted Vols in QuantLib

[Goutham M. <gou...@gm...>]

Hi all,

I am pricing FX options and was going through the Quantlib Python Guide to
assist me.
I came to this section on DeltaVolQuotes:

deltaType = ql.DeltaVolQuote.Fwd # Also supports: Spot, PaSpot, PaFwd
atmType = ql.DeltaVolQuote.AtmFwd # Also supports: AtmSpot,
AtmDeltaNeutral, AtmVegaMax, AtmGammaMax,␣
˓→AtmPutCall50
maturity = 1.0
volAtm, vol25DeltaCall, vol25DeltaPut = 0.08, 0.075, 0.095
atmDeltaQuote = ql.DeltaVolQuote(ql.QuoteHandle(ql.SimpleQuote(volAtm)),
deltaType, maturity, atmType)
vol25DeltaPutQuote = ql.DeltaVolQuote(-0.25,
ql.QuoteHandle(ql.SimpleQuote(vol25DeltaPut)), maturity,␣
˓→deltaType)
vol25DeltaCallQuote = ql.DeltaVolQuote(0.25,
ql.QuoteHandle(ql.SimpleQuote(vol25DeltaCall)), maturity,␣
˓→deltaType)

I have the ATMf and 25DRR and 25D Fly quotes for various tenors as attached
in the table below. My doubt is using the code above, is there any way I
can construct the volatility smile curve using QuantLib to derive the
implied volatility of an option.

Vol Table:
ATMF Vol 25D RR 25D Std 10D RR 10 Std
Bid Ask
1m 6.1 6.45 0.25 0.5 0.05 0.15 0.45 0.9 0.16 0.48
2m 6.05 6.4 0.3 0.55 0.1 0.25 0.54 0.99 0.32 0.8
3m 6.05 6.3 0.35 0.6 0.15 0.35 0.63 1.08 0.48 1.12
6m 5.9 6.1 0.45 0.65 0.2 0.35 0.81 1.17 0.64 1.12
9m 5.9 6.1 0.45 0.7 0.2 0.35 0.81 1.26 0.64 1.12
1y 5.9 6.1 0.5 0.7 0.2 0.4 0.9 1.26 0.64 1.28
2y 5.5 6 0.3 0.7 0.25 0.55 0.54 1.26 0.8 1.76
3y 5.2 5.7 0.3 0.7 0.3 0.7 0.54 1.26 0.96 2.24
4y 5.1 5.6 0.1 0.7 0.35 0.85 0.18 1.26 1.12 2.72
5y 5 5.5 -0.1 0.4 0.5 0.9 -0.18 0.72 1.6 2.88

Thank You,
Best,
Goutham

[Quantlib-users] makewhole call for bonds

[jian Xu <jia...@gm...>]

Hi,

The makewhole call features are very common for bonds in the US.  Is
there a way to calculate bond price to yield given a makewhole feature
(ie, makewhole date, spread, price)?  Thanks.

Jian

Re: [Quantlib-users] setting boundary conditions in FdmBackwardSolver

[Klaus S. <kl...@sp...>]

Hi 

you can define a set of boundary conditions like this

bcSet = ql.FdmBoundaryConditionSet(
    [ql.FdmDirichletBoundary(mesher, 100, 0, ql.FdmBoundaryCondition.Lower),
     ql.FdmDirichletBoundary(mesher, 200, 0, ql.FdmBoundaryCondition.Upper)]
)

Second parameter of the Dirichlet boundary condition is the value of the underlying, third parameter the direction of the boundary condition. For one dimensional problems this is always zero.

regards
Klaus

On Montag, 7. Februar 2022 17:24:32 CET Yev via QuantLib-users wrote:

Hi,


I have this Python code:


    solver = ql.FdmBackwardSolver(
        ql.FdmLinearOpCompositeProxy(op),
        ql.FdmBoundaryConditionSet(),
        ql.FdmStepConditionComposite(ql.DoubleVector(), ql.FdmStepConditionVector()),
        ql.FdmSchemeDesc.CrankNicolson()
    )


How can I specify left/write boundary conditions, let's say left=1, right = 2?


It's probably in this argument: ql.FdmBoundaryConditionSet(), but I cannot figure it out.

[Quantlib-users] setting boundary conditions in FdmBackwardSolver

[Yev <ye...@ya...>]

Hi,
I have this Python code:
    solver = ql.FdmBackwardSolver(
        ql.FdmLinearOpCompositeProxy(op),
        ql.FdmBoundaryConditionSet(),
        ql.FdmStepConditionComposite(ql.DoubleVector(), ql.FdmStepConditionVector()),
        ql.FdmSchemeDesc.CrankNicolson()
    )
How can I specify left/write boundary conditions, let's say left=1, right = 2?
It's probably in this argument: ql.FdmBoundaryConditionSet(), but I cannot figure it out.

Re: [Quantlib-users] Heston Calibration for American

[Anders J. <and...@ti...>]

Hi,

Great, thanks, I'll  give the FdHestonVanillaEngine a try.

BR,
//A

On Thu, Feb 3, 2022 at 5:21 PM Luigi Ballabio <lui...@gm...>
wrote:

> Hello,
>     the calibration implemented in QuantLib (as in the
> `HestonModel::calibrate` method) can only use European prices.  However,
> the engine pointed out by Jonathan can be used if you have some external
> calibration machinery; you would write an objective function that takes the
> Heston parameters as input, creates the model and the engine, prices the
> quoted options, and returns the difference between calculated and quoted
> price.  You can read something like this in <
> http://gouthamanbalaraman.com/blog/heston-calibration-scipy-optimize-quantlib-python.html>
> (except you'd have to use VanillaOption and FdHestonVanillaEngine instead
> of HestonModelHelper).
>
> Hope this helps,
>     Luigi
>
>
>
> On Thu, Feb 3, 2022 at 11:58 AM Jonathan Sweemer <sw...@gm...>
> wrote:
>
>> Hi Anders,
>>
>> Looks like there is some code in the QuantLib test suite for
>> American-style vanilla Heston pricing using FD [1]. You might be able to
>> reuse some of this code for calibration purposes as well.
>>
>> [1]
>> https://github.com/lballabio/QuantLib/blob/master/test-suite/hestonmodel.cpp#L665
>>
>> On Wed, Feb 2, 2022 at 4:33 AM Anders Johansson <
>> and...@ti...> wrote:
>>
>>> Hi,
>>>
>>> Is there a way to calibrate a heston model to american options using QL?
>>> Which variety is the fastest?
>>>
>>> Thank you very much.
>>>
>>> //A
>>>
>>> _______________________________________________
>>> 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] Heston Calibration for American

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

Hello,
    the calibration implemented in QuantLib (as in the
`HestonModel::calibrate` method) can only use European prices.  However,
the engine pointed out by Jonathan can be used if you have some external
calibration machinery; you would write an objective function that takes the
Heston parameters as input, creates the model and the engine, prices the
quoted options, and returns the difference between calculated and quoted
price.  You can read something like this in <
http://gouthamanbalaraman.com/blog/heston-calibration-scipy-optimize-quantlib-python.html>
(except you'd have to use VanillaOption and FdHestonVanillaEngine instead
of HestonModelHelper).

Hope this helps,
    Luigi



On Thu, Feb 3, 2022 at 11:58 AM Jonathan Sweemer <sw...@gm...> wrote:

> Hi Anders,
>
> Looks like there is some code in the QuantLib test suite for
> American-style vanilla Heston pricing using FD [1]. You might be able to
> reuse some of this code for calibration purposes as well.
>
> [1]
> https://github.com/lballabio/QuantLib/blob/master/test-suite/hestonmodel.cpp#L665
>
> On Wed, Feb 2, 2022 at 4:33 AM Anders Johansson <
> and...@ti...> wrote:
>
>> Hi,
>>
>> Is there a way to calibrate a heston model to american options using QL?
>> Which variety is the fastest?
>>
>> Thank you very much.
>>
>> //A
>>
>> _______________________________________________
>> 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] Heston Calibration for American

[Jonathan S. <sw...@gm...>]

Hi Anders,

Looks like there is some code in the QuantLib test suite for American-style
vanilla Heston pricing using FD [1]. You might be able to reuse some of
this code for calibration purposes as well.

[1]
https://github.com/lballabio/QuantLib/blob/master/test-suite/hestonmodel.cpp#L665

On Wed, Feb 2, 2022 at 4:33 AM Anders Johansson <and...@ti...>
wrote:

> Hi,
>
> Is there a way to calibrate a heston model to american options using QL?
> Which variety is the fastest?
>
> Thank you very much.
>
> //A
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Difference between OAS function and cleanPriceOAS

[Sumit S. <su...@mo...>]

Sorry please ignore my previous email

Thanks

On Wed, 2 Feb 2022 at 20:15, Sumit Sengupta <su...@mo...>
wrote:

> Hi,
>    Just wondering what’s the difference in the 2 functions from a callable
> bond perspective.
>
> Thanks
> Sumit
> --
> Mosaic Smart Data
>
> mobile +44 (0)7961839363
> su...@mo...
> 25 Finsbury Circus ▫ EC2M 7EE ▫ London ▫ United Kingdom
> www.mosaicsmartdata.com
>
-- 
Mosaic Smart Data

mobile +44 (0)7961839363
su...@mo...
25 Finsbury Circus ▫ EC2M 7EE ▫ London ▫ United Kingdom
www.mosaicsmartdata.com

[Quantlib-users] Difference between OAS function and cleanPriceOAS

[Sumit S. <su...@mo...>]

Hi,
   Just wondering what’s the difference in the 2 functions from a callable
bond perspective.

Thanks
Sumit
-- 
Mosaic Smart Data

mobile +44 (0)7961839363
su...@mo...
25 Finsbury Circus ▫ EC2M 7EE ▫ London ▫ United Kingdom
www.mosaicsmartdata.com

Re: [Quantlib-users] Delayed payment dates for swaps and FRN

[Paul B. <p.b...@gm...>]

Hi Ben & Luigi

Many thanks for your hints. I will try this.

Kind regards
Paul

On 02.02.22 11:24, Luigi Ballabio wrote:
> Hello,
>     the `OvernightLeg` class now has a `withPaymentLag` method that
> allows to specify a number of payment days after the end date of the
> coupon, and the `OvernightIndexedSwap` class takes a similar parameter
> at its constructor, so swaps on RFR should be covered.
>
> There's still no such parameter for `VanillaSwap`, but `IborLeg` has
> `withPaymentLag`, so a workaround for floating-rate swaps (or bonds)
> would be to build the fixed and floating leg explicitly and pass them
> to the `Swap` class constructor.
>
> Hope this helps,
>     Luigi
>
>
> On Wed, Feb 2, 2022 at 9:46 AM Paul Buettiker <p.b...@gm...>
> wrote:
>
>     Dear all
>
>     With the advent of swaps on RFR, the terms of payment for some
>     currencies changed: the two legs now may have delayed payment
>     dates, i.e. the pay date is n days after the accrual end date (see
>     e.g.
>     https://www.snb.ch/n/mmr/reference/template_20191202/source/template_20191202.n.pdf
>     ). BTW, the same also holds for some floating rate notes.
>
>     According to an older statement of Luigi (
>     https://stackoverflow.com/questions/39789483/c-quantlib-swap-payment-dates),
>     there was no (easy) way to account for such delayed payment.
>
>     Are there plans to account for such delays? Is there a workaround
>     (sorry, I'm not yet very experienced with QuantLib)?
>
>     Kind regards,
>     Paul.
>     _______________________________________________
>     QuantLib-users mailing list
>     Qua...@li...
>     https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Delayed payment dates for swaps and FRN

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

Hello,
    the `OvernightLeg` class now has a `withPaymentLag` method that allows
to specify a number of payment days after the end date of the coupon, and
the `OvernightIndexedSwap` class takes a similar parameter at its
constructor, so swaps on RFR should be covered.

There's still no such parameter for `VanillaSwap`, but `IborLeg` has
`withPaymentLag`, so a workaround for floating-rate swaps (or bonds) would
be to build the fixed and floating leg explicitly and pass them to the
`Swap` class constructor.

Hope this helps,
    Luigi


On Wed, Feb 2, 2022 at 9:46 AM Paul Buettiker <p.b...@gm...> wrote:

> Dear all
>
> With the advent of swaps on RFR, the terms of payment for some currencies
> changed: the two legs now may have delayed payment dates, i.e. the pay date
> is n days after the accrual end date (see e.g.
> https://www.snb.ch/n/mmr/reference/template_20191202/source/template_20191202.n.pdf
> ). BTW, the same also holds for some floating rate notes.
>
> According to an older statement of Luigi (
> https://stackoverflow.com/questions/39789483/c-quantlib-swap-payment-dates),
> there was no (easy) way to account for such delayed payment.
>
> Are there plans to account for such delays? Is there a workaround (sorry,
> I'm not yet very experienced with QuantLib)?
>
> Kind regards,
> Paul.
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Latest version of Quantlib for Python 3.5.2

[Ben W. <ben...@ma...>]

Thanks Luigi, I was afraid this might be the case. By the looks of things we might be able to get the server upgraded to Python 3.7 which would save building my own.

 

Regards

Bne

 

From: Luigi Ballabio <lui...@gm...> 
Sent: Wednesday, 2 February 2022 7:47 PM
To: Ben Watson <ben...@ma...>
Cc: QuantLib users <qua...@li...>
Subject: Re: [Quantlib-users] Latest version of Quantlib for Python 3.5.2

 

Hello Ben,

    I'm not aware of other binary repositories.

 

Luigi

 

 

On Wed, Feb 2, 2022 at 5:22 AM Ben Watson <ben...@ma... <mailto:ben...@ma...> > wrote:

Hi,

I am executing Quantlib python scripts from an SQL server. I have a working
script running in a local UI running Python 3.8 with out issue. However on
the SQL server I get the error module 'QuantLib' has no attribute
'OvernightLeg'.

Looking at the installed version it is telling me quantlib version 1.16.1
which I am guessing predates a number of features like OvernightLeg's. Are
there any later versions of pre-complied Quantlib that are Python 3.5.2
compliant floating around the web, or am I in the world of compiling up a
version?

Regards

Ben




_______________________________________________
QuantLib-users mailing list
Qua...@li... <mailto:Qua...@li...> 
https://lists.sourceforge.net/lists/listinfo/quantlib-users

Re: [Quantlib-users] Delayed payment dates for swaps and FRN

[Ben W. <ben...@ma...>]

 

I have been looking at similar issues with XCCY ASW swaps and RFR rates. Manually built custom swap legs made with SimpleCashFlow to offset coupon amounts and manually calculate stub accruals etc.

 

Regards

 

Ben

 

 

From: Paul Buettiker <p.b...@gm...> 
Sent: Wednesday, 2 February 2022 7:43 PM
To: qua...@li...
Subject: [Quantlib-users] Delayed payment dates for swaps and FRN

 

Dear all

 

With the advent of swaps on RFR, the terms of payment for some currencies changed: the two legs now may have delayed payment dates, i.e. the pay date is n days after the accrual end date (see e.g. https://www.snb.ch/n/mmr/reference/template_20191202/source/template_20191202.n.pdf ). BTW, the same also holds for some floating rate notes.

 

According to an older statement of Luigi ( https://stackoverflow.com/questions/39789483/c-quantlib-swap-payment-dates), there was no (easy) way to account for such delayed payment.

 

Are there plans to account for such delays? Is there a workaround (sorry, I'm not yet very experienced with QuantLib)?

 

Kind regards,

Paul.

Re: [Quantlib-users] Latest version of Quantlib for Python 3.5.2

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

Hello Ben,
    I'm not aware of other binary repositories.

Luigi


On Wed, Feb 2, 2022 at 5:22 AM Ben Watson <ben...@ma...>
wrote:

> Hi,
>
> I am executing Quantlib python scripts from an SQL server. I have a working
> script running in a local UI running Python 3.8 with out issue. However on
> the SQL server I get the error module 'QuantLib' has no attribute
> 'OvernightLeg'.
>
> Looking at the installed version it is telling me quantlib version 1.16.1
> which I am guessing predates a number of features like OvernightLeg's. Are
> there any later versions of pre-complied Quantlib that are Python 3.5.2
> compliant floating around the web, or am I in the world of compiling up a
> version?
>
> Regards
>
> Ben
>
>
>
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

[Quantlib-users] Delayed payment dates for swaps and FRN

[Paul B. <p.b...@gm...>]

Dear all

With the advent of swaps on RFR, the terms of payment for some currencies
changed: the two legs now may have delayed payment dates, i.e. the pay date
is n days after the accrual end date (see e.g.
https://www.snb.ch/n/mmr/reference/template_20191202/source/template_20191202.n.pdf
). BTW, the same also holds for some floating rate notes.

According to an older statement of Luigi (
https://stackoverflow.com/questions/39789483/c-quantlib-swap-payment-dates),
there was no (easy) way to account for such delayed payment.

Are there plans to account for such delays? Is there a workaround (sorry,
I'm not yet very experienced with QuantLib)?

Kind regards,
Paul.

[Quantlib-users] Latest version of Quantlib for Python 3.5.2

[Ben W. <ben...@ma...>]

Hi,

I am executing Quantlib python scripts from an SQL server. I have a working
script running in a local UI running Python 3.8 with out issue. However on
the SQL server I get the error module 'QuantLib' has no attribute
'OvernightLeg'.

Looking at the installed version it is telling me quantlib version 1.16.1
which I am guessing predates a number of features like OvernightLeg's. Are
there any later versions of pre-complied Quantlib that are Python 3.5.2
compliant floating around the web, or am I in the world of compiling up a
version?

Regards

Ben

[Quantlib-users] Heston Calibration for American

[Anders J. <and...@ti...>]

Hi,

Is there a way to calibrate a heston model to american options using QL?
Which variety is the fastest?

Thank you very much.

//A

[Quantlib-users] Risk Ladder

[Will S. <Wil...@as...>]

Hi there, does anyone know if there is a function in Quantlib Excel that would allow me to aggregate a series of Bond handles and see their aggregate deltas split out by separate tenor buckets? (Presumably if there was it would be the tenor buckets as defined by a specific discount curve that I would have to build)

Thanks in advance for any thoughts & have a good weekend,

Will

Re: [Quantlib-users] Diffusion PDE with Crank-Nicolson

[Klaus S. <kl...@sp...>]

Hi Yevgeniy

don't know if QuantLib would be my first choise to solve general PDEs but this script below does the job. Defintion of D(x,t) and the heat diffusion equation is taken e.g. from https://www.uni-muenster.de/imperia/md/content/physik_tp/lectures/ws2016-2017/num_methods_i/heat.pdf 


from typing import List

import QuantLib as ql
import matplotlib.pyplot as plt
import numpy as np


class HeatDiffusionOperator:
    def __init__(self, t_max: float, x_values: List[float]):
        self.__mesher = ql.FdmMesherComposite(
            ql.Predefined1dMesher(x_values)
        )
        self.__t_max = t_max
        self.__n = len(x_values)
        self.__x_values = ql.Array(x_values)

        self.__dx = ql.FirstDerivativeOp(0, self.__mesher)
        self.__ddx = ql.SecondDerivativeOp(0, self.__mesher)
        self.__map = ql.TripleBandLinearOp(0, self.__mesher)

    def size(self) -> int:
        return 1

    def setTime(self, t1: float, t2: float):
        t = 0.5 * (t1 + t2)

        self.__map.axpyb(
            ql.Array(1, 1.0),
            self.__dx,
            self.__ddx.mult(self.__x_values + ql.Array(self.__n, self.__t_max - t)),
            ql.Array()
        )

    def apply(self, r: ql.Array) -> ql.Array:
        return self.__map.apply(r)

    def solve_splitting(self, direction: int, r: ql.Array, dt: float) -> ql.Array:
        return self.__map.solve_splitting(r, dt, 1.0)


if __name__ == "__main__":
    n_points = 400
    n_time_steps = 500

    x_min = 0
    x_max = 0.4
    t_max = 0.02

    x_values = np.arange(x_min, x_max + 1e-8, (x_max - x_min) / (n_points - 1))
    y_values = ql.Array(list(np.exp(-(x_values - 0.5 * (x_max + x_min)) ** 2 / 1e-4)))

    op = HeatDiffusionOperator(t_max, list(x_values))

    solver = ql.FdmBackwardSolver(
        ql.FdmLinearOpCompositeProxy(op),
        ql.FdmBoundaryConditionSet(),
        ql.FdmStepConditionComposite(ql.DoubleVector(), ql.FdmStepConditionVector()),
        ql.FdmSchemeDesc.CrankNicolson()
    )

    solver.rollback(y_values, t_max, 0.0, n_time_steps, 0)

    plt.plot(x_values, y_values)
    plt.show()



On Dienstag, 25. Januar 2022 23:39:57 CET Yev via QuantLib-users wrote:

Hi,


I have a Python code that solves a 1-dimentional Heat Diffusion PDE.
My PDE has Diffusion Coefficient D(x,t) = x+t
My grid has 400 points in range [0,0.4]
I have a set of x values at time 0, and I iterate for 500 time steps.


I'd like to rewrite it using Quantlib, as I hope it will speed up my process.  
I looked through documentation and source code, but couldn't figure out what classes should I use to implement it.


Can you point me to how such problem can be solved with Quantlib?


Thank you,


Yevgeniy