quantlib-users — QuantLib users discussion list

Re: [Quantlib-users] Testing of new changes in Python

[Ashish B. <ash...@gm...>]

Thanks all for the detailed explanation.

Jack,
I am also interested in getting the spot Delta & Gamma. Would be good if
this could be included in the engine itself. Till then we will use the
bumping approach as suggested by Jonathan and Luigi.

Just to confirm, will the TW engine, which is part of 1.27, be exposed in
SWIG in the same release?

Thanks
Ashish

On Tue, 28 Jun 2022 at 14:54, Jack G <jac...@gm...> wrote:

> For asian options it's not exactly obvious what forward delta means as
> there are several fixing dates, but spot delta should be straightforward
> enough.
>
> Moment matching uses an analytic black formula (
> https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/pricingengines/asian/turnbullwakemanasianengine.cpp#L118)
> and everything is linear in spot so spot delta should be as easy as
> evaluating the black calculator delta at the same time as calling the price.
>
> There are a few edge cases (eg. if prior fixings mean it's already
> in-the-money) that need to be considered.
>
> I'm doing some experiments now to confirm this, I can look at implementing
> this and exposing them in SWIG but can't really commit to a timeline for it
> right now.
>
> On Tue, Jun 28, 2022 at 3:27 PM Luigi Ballabio <lui...@gm...>
> wrote:
>
>> In the FD case, the solver doesn't bump and reprice; at the end of the
>> roll-back calculation, the result is a grid of values at different values
>> of the underlying.  When the delta and gamma call `valueAt` repeatedly,
>> they're only interpolating at different points on that grid.  Compared to
>> the FD simulation the engine just did, it doesn't give a noticeable
>> overhead.
>>
>> However, this is specific to a FD simulation, so it can't be applied to
>> `TurnbullWakemanAsianEngine` which uses an analytic approximation.  In
>> principle, it might be possible to differentiate it, but it's probably
>> easier to bump and reprice as Jonathan said.
>>
>> Luigi
>>
>>
>> On Tue, Jun 28, 2022 at 12:50 AM Jonathan Sweemer <sw...@gm...>
>> wrote:
>>
>>> Hi Ashish,
>>>
>>> The FdBlackScholesAsianEngine calculates greeks by bumping the spot and
>>> repricing[1] so you should be able to do the same in your code.
>>>
>>> I’m actually not convinced it’s a good idea to always include the greeks
>>> in the result. In the case of FdBlackScholesAsianEngine it means the solver
>>> is called six times - once for the price, twice for the delta, and three
>>> times for the gamma. Perhaps there should be a way to tell the engine only
>>> to return the price to avoid the overhead of calculating the greeks for
>>> people who don’t need them.
>>>
>>> [1]
>>> https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/methods/finitedifferences/solvers/fdmsimple2dbssolver.cpp#L53
>>>
>>> 2022년 6월 27일 (월) 21:54, Ashish Bansal <ash...@gm...>님이 작성:
>>>
>>>> Hi
>>>>
>>>> Including Partha from my team who is trying to install it and test
>>>> 1.27.
>>>>
>>>> Luigi,
>>>>
>>>> I looked at the code and noticed that the
>>>> *turnbullwakemanasianengine.cpp* doesn't include the Delta or Gamma in
>>>> the results. Could including the solver for Delta and Gamma work, how it is
>>>> included in fdblackscholesasianengine.cpp?
>>>>         results_.delta = solver->deltaAt(spot, avg, spot*0.01);
>>>>         results_.gamma = solver->gammaAt(spot, avg, spot*0.01);
>>>>
>>>> Regards,
>>>> Ashish
>>>>
>>>> On Fri, 24 Jun 2022 at 11:38, Ashish Bansal <ash...@gm...>
>>>> wrote:
>>>>
>>>>> I can understand. You are doing great work for the community by
>>>>> maintaining this library. Thanks for it.
>>>>>
>>>>> Let me try building using SWIG as per instructions on this page.
>>>>> Thanks again for info.
>>>>>
>>>>> Regards,
>>>>> Ashish
>>>>>
>>>>> On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
>>>>> wrote:
>>>>>
>>>>>> Hello Ansish,
>>>>>>     no, I can not commit to a date for the release.  Other issues at
>>>>>> my job might take priority over the release.
>>>>>>
>>>>>> You can try the changes before the release by checking out the code
>>>>>> from GitHub and following the instructions at <
>>>>>> https://www.quantlib.org/install.shtml> to compile it.
>>>>>>
>>>>>> Hope this helps,
>>>>>>     Luigi
>>>>>>
>>>>>>
>>>>>> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <
>>>>>> ash...@gm...> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> We want to test the TurnbullWakemanAsianEngine using Python. How can
>>>>>>> we test the new changes through Python SWIG?
>>>>>>>
>>>>>>> Could v1.27 be released a little early, say by 10th July, if
>>>>>>> possible?
>>>>>>>
>>>>>>> Regards
>>>>>>> Ashish Bansal
>>>>>>> _______________________________________________
>>>>>>> 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 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] Testing of new changes in Python

[Jack G <jac...@gm...>]

For asian options it's not exactly obvious what forward delta means as
there are several fixing dates, but spot delta should be straightforward
enough.

Moment matching uses an analytic black formula (
https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/pricingengines/asian/turnbullwakemanasianengine.cpp#L118)
and everything is linear in spot so spot delta should be as easy as
evaluating the black calculator delta at the same time as calling the price.

There are a few edge cases (eg. if prior fixings mean it's already
in-the-money) that need to be considered.

I'm doing some experiments now to confirm this, I can look at implementing
this and exposing them in SWIG but can't really commit to a timeline for it
right now.

On Tue, Jun 28, 2022 at 3:27 PM Luigi Ballabio <lui...@gm...>
wrote:

> In the FD case, the solver doesn't bump and reprice; at the end of the
> roll-back calculation, the result is a grid of values at different values
> of the underlying.  When the delta and gamma call `valueAt` repeatedly,
> they're only interpolating at different points on that grid.  Compared to
> the FD simulation the engine just did, it doesn't give a noticeable
> overhead.
>
> However, this is specific to a FD simulation, so it can't be applied to
> `TurnbullWakemanAsianEngine` which uses an analytic approximation.  In
> principle, it might be possible to differentiate it, but it's probably
> easier to bump and reprice as Jonathan said.
>
> Luigi
>
>
> On Tue, Jun 28, 2022 at 12:50 AM Jonathan Sweemer <sw...@gm...>
> wrote:
>
>> Hi Ashish,
>>
>> The FdBlackScholesAsianEngine calculates greeks by bumping the spot and
>> repricing[1] so you should be able to do the same in your code.
>>
>> I’m actually not convinced it’s a good idea to always include the greeks
>> in the result. In the case of FdBlackScholesAsianEngine it means the solver
>> is called six times - once for the price, twice for the delta, and three
>> times for the gamma. Perhaps there should be a way to tell the engine only
>> to return the price to avoid the overhead of calculating the greeks for
>> people who don’t need them.
>>
>> [1]
>> https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/methods/finitedifferences/solvers/fdmsimple2dbssolver.cpp#L53
>>
>> 2022년 6월 27일 (월) 21:54, Ashish Bansal <ash...@gm...>님이 작성:
>>
>>> Hi
>>>
>>> Including Partha from my team who is trying to install it and test 1.27.
>>>
>>> Luigi,
>>>
>>> I looked at the code and noticed that the
>>> *turnbullwakemanasianengine.cpp* doesn't include the Delta or Gamma in
>>> the results. Could including the solver for Delta and Gamma work, how it is
>>> included in fdblackscholesasianengine.cpp?
>>>         results_.delta = solver->deltaAt(spot, avg, spot*0.01);
>>>         results_.gamma = solver->gammaAt(spot, avg, spot*0.01);
>>>
>>> Regards,
>>> Ashish
>>>
>>> On Fri, 24 Jun 2022 at 11:38, Ashish Bansal <ash...@gm...>
>>> wrote:
>>>
>>>> I can understand. You are doing great work for the community by
>>>> maintaining this library. Thanks for it.
>>>>
>>>> Let me try building using SWIG as per instructions on this page. Thanks
>>>> again for info.
>>>>
>>>> Regards,
>>>> Ashish
>>>>
>>>> On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
>>>> wrote:
>>>>
>>>>> Hello Ansish,
>>>>>     no, I can not commit to a date for the release.  Other issues at
>>>>> my job might take priority over the release.
>>>>>
>>>>> You can try the changes before the release by checking out the code
>>>>> from GitHub and following the instructions at <
>>>>> https://www.quantlib.org/install.shtml> to compile it.
>>>>>
>>>>> Hope this helps,
>>>>>     Luigi
>>>>>
>>>>>
>>>>> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <
>>>>> ash...@gm...> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> We want to test the TurnbullWakemanAsianEngine using Python. How can
>>>>>> we test the new changes through Python SWIG?
>>>>>>
>>>>>> Could v1.27 be released a little early, say by 10th July, if possible?
>>>>>>
>>>>>> Regards
>>>>>> Ashish Bansal
>>>>>> _______________________________________________
>>>>>> 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 mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Schedule from vector of dates in Python

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

Hi Luigi – that works… thank you

 

 

From: Luigi Ballabio <lui...@gm...> 
Sent: Tuesday, 28 June 2022 6:03 PM
To: Ben Watson <ben...@ma...>
Cc: QuantLib users <qua...@li...>
Subject: Re: [Quantlib-users] Schedule from vector of dates in Python

 

You should be able to use the constructor defined at <https://github.com/lballabio/QuantLib-SWIG/blob/master/SWIG/scheduler.i#L65>.

Please let me know if that doesn't work.

 

Luigi

 

 

On Tue, Jun 28, 2022 at 9:39 AM Ben Watson <ben...@ma... <mailto:ben...@ma...> > wrote:

 

Hi,

 

I suspect that the answer is no, but I am trying to build in QL Python a bond from a given schedule of dates (provided from another system).  Is this possible??

 

I believe that it is possible in C++ and QuantlibXL.

 

Regards

 

Ben

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

Re: [Quantlib-users] Schedule from vector of dates in Python

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

You should be able to use the constructor defined at <
https://github.com/lballabio/QuantLib-SWIG/blob/master/SWIG/scheduler.i#L65
>.
Please let me know if that doesn't work.

Luigi


On Tue, Jun 28, 2022 at 9:39 AM Ben Watson <ben...@ma...>
wrote:

>
>
> Hi,
>
>
>
> I suspect that the answer is no, but I am trying to build in QL Python a
> bond from a given schedule of dates (provided from another system).  Is
> this possible??
>
>
>
> I believe that it is possible in C++ and QuantlibXL.
>
>
>
> Regards
>
>
>
> Ben
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

[Quantlib-users] Schedule from vector of dates in Python

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

 

Hi,

 

I suspect that the answer is no, but I am trying to build in QL Python a bond from a given schedule of dates (provided from another system).  Is this possible??

 

I believe that it is possible in C++ and QuantlibXL.

 

Regards

 

Ben

Re: [Quantlib-users] Testing of new changes in Python

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

In the FD case, the solver doesn't bump and reprice; at the end of the
roll-back calculation, the result is a grid of values at different values
of the underlying.  When the delta and gamma call `valueAt` repeatedly,
they're only interpolating at different points on that grid.  Compared to
the FD simulation the engine just did, it doesn't give a noticeable
overhead.

However, this is specific to a FD simulation, so it can't be applied to
`TurnbullWakemanAsianEngine` which uses an analytic approximation.  In
principle, it might be possible to differentiate it, but it's probably
easier to bump and reprice as Jonathan said.

Luigi


On Tue, Jun 28, 2022 at 12:50 AM Jonathan Sweemer <sw...@gm...> wrote:

> Hi Ashish,
>
> The FdBlackScholesAsianEngine calculates greeks by bumping the spot and
> repricing[1] so you should be able to do the same in your code.
>
> I’m actually not convinced it’s a good idea to always include the greeks
> in the result. In the case of FdBlackScholesAsianEngine it means the solver
> is called six times - once for the price, twice for the delta, and three
> times for the gamma. Perhaps there should be a way to tell the engine only
> to return the price to avoid the overhead of calculating the greeks for
> people who don’t need them.
>
> [1]
> https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/methods/finitedifferences/solvers/fdmsimple2dbssolver.cpp#L53
>
> 2022년 6월 27일 (월) 21:54, Ashish Bansal <ash...@gm...>님이 작성:
>
>> Hi
>>
>> Including Partha from my team who is trying to install it and test 1.27.
>>
>> Luigi,
>>
>> I looked at the code and noticed that the
>> *turnbullwakemanasianengine.cpp* doesn't include the Delta or Gamma in
>> the results. Could including the solver for Delta and Gamma work, how it is
>> included in fdblackscholesasianengine.cpp?
>>         results_.delta = solver->deltaAt(spot, avg, spot*0.01);
>>         results_.gamma = solver->gammaAt(spot, avg, spot*0.01);
>>
>> Regards,
>> Ashish
>>
>> On Fri, 24 Jun 2022 at 11:38, Ashish Bansal <ash...@gm...>
>> wrote:
>>
>>> I can understand. You are doing great work for the community by
>>> maintaining this library. Thanks for it.
>>>
>>> Let me try building using SWIG as per instructions on this page. Thanks
>>> again for info.
>>>
>>> Regards,
>>> Ashish
>>>
>>> On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
>>> wrote:
>>>
>>>> Hello Ansish,
>>>>     no, I can not commit to a date for the release.  Other issues at my
>>>> job might take priority over the release.
>>>>
>>>> You can try the changes before the release by checking out the code
>>>> from GitHub and following the instructions at <
>>>> https://www.quantlib.org/install.shtml> to compile it.
>>>>
>>>> Hope this helps,
>>>>     Luigi
>>>>
>>>>
>>>> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <ash...@gm...>
>>>> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> We want to test the TurnbullWakemanAsianEngine using Python. How can
>>>>> we test the new changes through Python SWIG?
>>>>>
>>>>> Could v1.27 be released a little early, say by 10th July, if possible?
>>>>>
>>>>> Regards
>>>>> Ashish Bansal
>>>>> _______________________________________________
>>>>> 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] Testing of new changes in Python

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

Hi Ashish,

The FdBlackScholesAsianEngine calculates greeks by bumping the spot and
repricing[1] so you should be able to do the same in your code.

I’m actually not convinced it’s a good idea to always include the greeks in
the result. In the case of FdBlackScholesAsianEngine it means the solver is
called six times - once for the price, twice for the delta, and three times
for the gamma. Perhaps there should be a way to tell the engine only to
return the price to avoid the overhead of calculating the greeks for people
who don’t need them.

[1]
https://github.com/lballabio/QuantLib/blob/cf8524b6d4004cb08fc5468524769e37984d8047/ql/methods/finitedifferences/solvers/fdmsimple2dbssolver.cpp#L53

2022년 6월 27일 (월) 21:54, Ashish Bansal <ash...@gm...>님이 작성:

> Hi
>
> Including Partha from my team who is trying to install it and test 1.27.
>
> Luigi,
>
> I looked at the code and noticed that the *turnbullwakemanasianengine.cpp*
> doesn't include the Delta or Gamma in the results. Could including the
> solver for Delta and Gamma work, how it is included
> in fdblackscholesasianengine.cpp?
>         results_.delta = solver->deltaAt(spot, avg, spot*0.01);
>         results_.gamma = solver->gammaAt(spot, avg, spot*0.01);
>
> Regards,
> Ashish
>
> On Fri, 24 Jun 2022 at 11:38, Ashish Bansal <ash...@gm...>
> wrote:
>
>> I can understand. You are doing great work for the community by
>> maintaining this library. Thanks for it.
>>
>> Let me try building using SWIG as per instructions on this page. Thanks
>> again for info.
>>
>> Regards,
>> Ashish
>>
>> On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
>> wrote:
>>
>>> Hello Ansish,
>>>     no, I can not commit to a date for the release.  Other issues at my
>>> job might take priority over the release.
>>>
>>> You can try the changes before the release by checking out the code from
>>> GitHub and following the instructions at <
>>> https://www.quantlib.org/install.shtml> to compile it.
>>>
>>> Hope this helps,
>>>     Luigi
>>>
>>>
>>> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <ash...@gm...>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> We want to test the TurnbullWakemanAsianEngine using Python. How can we
>>>> test the new changes through Python SWIG?
>>>>
>>>> Could v1.27 be released a little early, say by 10th July, if possible?
>>>>
>>>> Regards
>>>> Ashish Bansal
>>>> _______________________________________________
>>>> 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] Testing of new changes in Python

[Ashish B. <ash...@gm...>]

Hi

Including Partha from my team who is trying to install it and test 1.27.

Luigi,

I looked at the code and noticed that the *turnbullwakemanasianengine.cpp*
doesn't include the Delta or Gamma in the results. Could including the
solver for Delta and Gamma work, how it is included
in fdblackscholesasianengine.cpp?
        results_.delta = solver->deltaAt(spot, avg, spot*0.01);
        results_.gamma = solver->gammaAt(spot, avg, spot*0.01);

Regards,
Ashish

On Fri, 24 Jun 2022 at 11:38, Ashish Bansal <ash...@gm...> wrote:

> I can understand. You are doing great work for the community by
> maintaining this library. Thanks for it.
>
> Let me try building using SWIG as per instructions on this page. Thanks
> again for info.
>
> Regards,
> Ashish
>
> On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
> wrote:
>
>> Hello Ansish,
>>     no, I can not commit to a date for the release.  Other issues at my
>> job might take priority over the release.
>>
>> You can try the changes before the release by checking out the code from
>> GitHub and following the instructions at <
>> https://www.quantlib.org/install.shtml> to compile it.
>>
>> Hope this helps,
>>     Luigi
>>
>>
>> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <ash...@gm...>
>> wrote:
>>
>>> Hi,
>>>
>>> We want to test the TurnbullWakemanAsianEngine using Python. How can we
>>> test the new changes through Python SWIG?
>>>
>>> Could v1.27 be released a little early, say by 10th July, if possible?
>>>
>>> Regards
>>> Ashish Bansal
>>> _______________________________________________
>>> QuantLib-users mailing list
>>> Qua...@li...
>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>
>>

Re: [Quantlib-users] Testing of new changes in Python

[Ashish B. <ash...@gm...>]

I can understand. You are doing great work for the community by maintaining
this library. Thanks for it.

Let me try building using SWIG as per instructions on this page. Thanks
again for info.

Regards,
Ashish

On Thu, 23 Jun 2022 at 19:36, Luigi Ballabio <lui...@gm...>
wrote:

> Hello Ansish,
>     no, I can not commit to a date for the release.  Other issues at my
> job might take priority over the release.
>
> You can try the changes before the release by checking out the code from
> GitHub and following the instructions at <
> https://www.quantlib.org/install.shtml> to compile it.
>
> Hope this helps,
>     Luigi
>
>
> On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <ash...@gm...>
> wrote:
>
>> Hi,
>>
>> We want to test the TurnbullWakemanAsianEngine using Python. How can we
>> test the new changes through Python SWIG?
>>
>> Could v1.27 be released a little early, say by 10th July, if possible?
>>
>> Regards
>> Ashish Bansal
>> _______________________________________________
>> QuantLib-users mailing list
>> Qua...@li...
>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>
>

Re: [Quantlib-users] AAD

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

Hi Ben,

Not as far as I know, no I don't think so.


regards,

Mo

On Thu, Jun 23, 2022 at 9:34 AM Ben Watson <ben...@ma...>
wrote:

> I have been looking at some applications where AAD might be applicable. I
> found some stuff for C++ Quantlib it was implemented by operator overlading
> (not sure about this last bit!).
>
>
>
> My question is… has any of the functionality of AAD been ported to
> Quantlib Python? If so – is there an example could be followed??
>
>
>
> Regards
>
>
>
> Ben
>
>
>
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>


-- 
Mohammad Shojatalab

[Quantlib-users] Swaptions volatilities

[El Y. F. <fah...@gm...>]

Hi,
now i am using swaption volatilities to calibrate HW 1 factor short rate
model.
but i am getting these data from a service provider and swaptions and swaps
are OTC so getting these type of data kinda hard.
I want to know if there is a way to generate swaption volatilities
(something like pricing swaps then swaption in quantlib and getting the
volatilities) or that's kind ambitious ?
Thank you.

Re: [Quantlib-users] Testing of new changes in Python

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

Hello Ansish,
    no, I can not commit to a date for the release.  Other issues at my job
might take priority over the release.

You can try the changes before the release by checking out the code from
GitHub and following the instructions at <
https://www.quantlib.org/install.shtml> to compile it.

Hope this helps,
    Luigi


On Thu, Jun 23, 2022 at 11:17 AM Ashish Bansal <ash...@gm...>
wrote:

> Hi,
>
> We want to test the TurnbullWakemanAsianEngine using Python. How can we
> test the new changes through Python SWIG?
>
> Could v1.27 be released a little early, say by 10th July, if possible?
>
> Regards
> Ashish Bansal
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

[Quantlib-users] Looking for consultancy on integrating Nelson-Siegel-Svensson

[Andrea P. <and...@so...>]

Hello group! 

We're looking for a shot-term consultancy on integrating Nelson-Siegel-Svensson fitting model (C++ QuantLib libraries) in a java pricing engine, already using C++ QuantLib libraries. 

Please contact me if some of you has the potential skill for this small project. 

Kind regards, 
Andrea 

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[Quantlib-users] Testing of new changes in Python

[Ashish B. <ash...@gm...>]

Hi,

We want to test the TurnbullWakemanAsianEngine using Python. How can we
test the new changes through Python SWIG?

Could v1.27 be released a little early, say by 10th July, if possible?

Regards
Ashish Bansal

[Quantlib-users] AAD

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

I have been looking at some applications where AAD might be applicable. I
found some stuff for C++ Quantlib it was implemented by operator overlading
(not sure about this last bit!).

 

My question is. has any of the functionality of AAD been ported to Quantlib
Python? If so - is there an example could be followed??

 

Regards

 

Ben

 

 

Re: [Quantlib-users] Callable bonds and HW model question

[Josh C. <jo...@rq...>]

Hi El Yousfi,
Just for #1. You can take look at below link. 
http://gouthamanbalaraman.com/blog/short-interest-rate-model-calibration-quantlib.html

On Wed, Jun 22, 2022, at 18:20, El Yousfi Fahd wrote:
> Hello,
> I am new to Quantlib, and I am trying to evaluate callable bonds and I am using the HW 1 factor model for short rates.
> for now i calibrate the model using swaptions volatilities
> and I have some questions:
>  1 - can I calibrate the model using swaption prices? If yes can you show me an example or guide me? I'm using python
> 2 - is the HW model good in this case or should i switch to another model? I tried the 2 factor model but for some reason it gives me an error when I use the Function callableBond.OAS()
> any kind of help would be appreciated
> thank you
> 
> 
> 
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
> 

Josh Chien
Einstein's law of modeling says "Make it as simple as possible, but not simpler."

[Quantlib-users] Callable bonds and HW model question

[El Y. F. <fah...@gm...>]

Hello,
I am new to Quantlib, and I am trying to evaluate callable bonds and I am
using the HW 1 factor model for short rates.
for now i calibrate the model using swaptions volatilities
and I have some questions:
 1 - can I calibrate the model using swaption prices? If yes can you show
me an example or guide me? I'm using python
2 - is the HW model good in this case or should i switch to another model?
I tried the 2 factor model but for some reason it gives me an error when I
use the Function callableBond.OAS()
any kind of help would be appreciated
thank you

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

[Ashish B. <ash...@gm...>]

I was thinking to handle the same way but wanted confirmation from some
expert. Thanks a lot for help.

I will open an issue in the github so that some code expert can fix it in
future.

Regards,
Ashish

On Tue, 21 Jun 2022 at 02:36, Jonathan Sweemer <sw...@gm...> wrote:

> If the product knocks in or out during its lifetime then it is no longer
> the same product - in the case of the knock-out it becomes nothing and in
> the case of the knock-in it becomes a regular option and should be priced
> as such. Your position management system should be able to handle these
> lifecycle events so that you see the right positions in your book.
>
> Having said that, I still think it could make sense to improve the
> validations on these barrier products to provide better error messages in
> case of invalid inputs.
>
>
> 2022년 6월 20일 (월) 22:50, Ashish Bansal <ash...@gm...>님이 작성:
>
>> I am not pricing with barrier breach on the inception day.
>>
>> Assuming the trade started out of barrier limit, i.e. higher than barrier
>> for DI and DO type, I am pricing during the life of trade. During life the
>> underlying could move knocking the barrier. The code starts showing error
>> after the breach.
>>
>> I am looking to improve code so that during life of trade it shows right
>> price output instead of error.
>>
>> Ashish
>>
>> On Mon, Jun 20, 2022, 4:16 PM Jonathan Sweemer <sw...@gm...> wrote:
>>
>>> I can reproduce the exception that you're seeing. As for the
>>> explanation, I'm not an expert in this code, but here's my take on it:
>>>
>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>>> this case? Can't the engine handle it?
>>>
>>> Generally you'd expect a down-and-out call to be quoted with spot above
>>> the barrier, so starting with spot below the barrier is kind of a
>>> degenerate case, as the product knocks out before it even starts, so I
>>> believe the exception is warranted (though perhaps not as informative as it
>>> could be).
>>>
>>> 2. For Down-in, I am getting the same error whereas the option gets
>>>> activated and is still live then why is this not priced by the engine? Am
>>>> I doing something wrong?
>>>
>>> Similar to question 1, you'd expect a down-and-in call to be quoted with
>>> spot above the barrier, but in this case I agree that a case can be made
>>> for the product to be live (i.e. knocked-in at inception), although it
>>> would probably depend on the specific deal terms how to handle this case.
>>> If you have any such deal terms to refer to, then you might want to submit
>>> a pull request to handle them accordingly in the code.
>>>
>>>
>>>
>>> On Mon, Jun 20, 2022 at 5:11 PM Ashish Bansal <ash...@gm...>
>>> wrote:
>>>
>>>> Following is the code that I am using to evaluate the barrier option.
>>>> Here if I pass the spot which has crossed the barrier for knock-in or
>>>> knock-out options, I get an error. The down-out is in the code and for
>>>> down-in, I took the example given in row# 266 of with spot price as 94 from:
>>>>
>>>> https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp
>>>>
>>>> My python code:
>>>>
>>>> ############################################################################
>>>> import QuantLib as ql
>>>>
>>>> # Setting Barrier option parameters
>>>> calculation_date = ql.Date(1, 6, 2021)
>>>> maturity_date = ql.Date(25, 11, 2021)
>>>> spot_price = 577.0
>>>> strike_price = 770.0
>>>> volatility = 43.56/100
>>>> option_type = ql.Option.Call
>>>> risk_free_rate = 0.175/100
>>>> barrier_price = 400.0
>>>> barrier_type = ql.Barrier.DownOut
>>>> rebate = 0 # no rebate
>>>> day_count = ql.Actual365Fixed()
>>>> calendar = ql.TARGET()
>>>>
>>>>
>>>> marketpremium = 18.64
>>>>
>>>> ql.Settings.instance().evaluationDate = calculation_date
>>>>
>>>> # Constructing the EU option in QL
>>>> payoff = ql.PlainVanillaPayoff(option_type, strike_price)
>>>> eu_exercise = ql.EuropeanExercise(maturity_date)
>>>> barrier_eu_option =
>>>> ql.BarrierOption(barrier_type,barrier_price,rebate,payoff, eu_exercise)
>>>>
>>>> # The Black-Scholes-Merton process is constructed
>>>> spot_handle = ql.QuoteHandle(
>>>> ql.SimpleQuote(spot_price)
>>>> )
>>>> flat_rate_ts = ql.YieldTermStructureHandle(
>>>> ql.FlatForward(calculation_date,
>>>>   risk_free_rate,
>>>>   day_count)
>>>> )
>>>> flat_vol_ts = ql.BlackVolTermStructureHandle(
>>>> ql.BlackConstantVol(calculation_date,
>>>> calendar,
>>>> volatility,
>>>> day_count)
>>>> )
>>>> bs_process = ql.BlackScholesProcess(spot_handle,
>>>>   flat_rate_ts,
>>>>   flat_vol_ts)
>>>>
>>>> # compute the theoretical price using the AnalyticEuropeanEngine
>>>>
>>>> barrier_eu_option.setPricingEngine(ql.FdBlackScholesBarrierEngine(bs_process))
>>>> br_eu_price = barrier_eu_option.NPV()
>>>> print("The theoretical price for  EU barrier is %lf" % br_eu_price)
>>>>
>>>> ############################################################################
>>>>
>>>> Regards,
>>>> Ashish
>>>>
>>>> On Fri, 17 Jun 2022 at 02:40, Jonathan Sweemer <sw...@gm...>
>>>> wrote:
>>>>
>>>>> Hi Ashish, can you share your code or a link to it?
>>>>>
>>>>> 2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Any guidance on this error in barriers?
>>>>>>
>>>>>> Regards,
>>>>>> Ashish
>>>>>>
>>>>>> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
>>>>>> wrote:
>>>>>>
>>>>>>> Dear team,
>>>>>>>
>>>>>>> I need some guidance on the pricing of the barrier option using
>>>>>>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>>>>>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>>>>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>>>>>> this case? Can't the engine handle it?
>>>>>>> 2. For Down-in, I am getting the same error whereas the option gets
>>>>>>> activated and is still live then why is this not priced by the engine? Am
>>>>>>> I doing something wrong?
>>>>>>>
>>>>>>> What is the difference between FdBlackScholesBarrierEngine
>>>>>>> and AnalyticBarrierEngine? I compared a few results and found a slight
>>>>>>> difference in values. Any suggestion on which one to use?
>>>>>>>
>>>>>>> Any help would be appreciated.
>>>>>>>
>>>>>>> Thanks
>>>>>>> Ashish
>>>>>>>
>>>>>> _______________________________________________
>>>>>> QuantLib-users mailing list
>>>>>> Qua...@li...
>>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>>>>
>>>>>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

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

If the product knocks in or out during its lifetime then it is no longer
the same product - in the case of the knock-out it becomes nothing and in
the case of the knock-in it becomes a regular option and should be priced
as such. Your position management system should be able to handle these
lifecycle events so that you see the right positions in your book.

Having said that, I still think it could make sense to improve the
validations on these barrier products to provide better error messages in
case of invalid inputs.


2022년 6월 20일 (월) 22:50, Ashish Bansal <ash...@gm...>님이 작성:

> I am not pricing with barrier breach on the inception day.
>
> Assuming the trade started out of barrier limit, i.e. higher than barrier
> for DI and DO type, I am pricing during the life of trade. During life the
> underlying could move knocking the barrier. The code starts showing error
> after the breach.
>
> I am looking to improve code so that during life of trade it shows right
> price output instead of error.
>
> Ashish
>
> On Mon, Jun 20, 2022, 4:16 PM Jonathan Sweemer <sw...@gm...> wrote:
>
>> I can reproduce the exception that you're seeing. As for the explanation,
>> I'm not an expert in this code, but here's my take on it:
>>
>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>> this case? Can't the engine handle it?
>>
>> Generally you'd expect a down-and-out call to be quoted with spot above
>> the barrier, so starting with spot below the barrier is kind of a
>> degenerate case, as the product knocks out before it even starts, so I
>> believe the exception is warranted (though perhaps not as informative as it
>> could be).
>>
>> 2. For Down-in, I am getting the same error whereas the option gets
>>> activated and is still live then why is this not priced by the engine? Am
>>> I doing something wrong?
>>
>> Similar to question 1, you'd expect a down-and-in call to be quoted with
>> spot above the barrier, but in this case I agree that a case can be made
>> for the product to be live (i.e. knocked-in at inception), although it
>> would probably depend on the specific deal terms how to handle this case.
>> If you have any such deal terms to refer to, then you might want to submit
>> a pull request to handle them accordingly in the code.
>>
>>
>>
>> On Mon, Jun 20, 2022 at 5:11 PM Ashish Bansal <ash...@gm...>
>> wrote:
>>
>>> Following is the code that I am using to evaluate the barrier option.
>>> Here if I pass the spot which has crossed the barrier for knock-in or
>>> knock-out options, I get an error. The down-out is in the code and for
>>> down-in, I took the example given in row# 266 of with spot price as 94 from:
>>>
>>> https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp
>>>
>>> My python code:
>>>
>>> ############################################################################
>>> import QuantLib as ql
>>>
>>> # Setting Barrier option parameters
>>> calculation_date = ql.Date(1, 6, 2021)
>>> maturity_date = ql.Date(25, 11, 2021)
>>> spot_price = 577.0
>>> strike_price = 770.0
>>> volatility = 43.56/100
>>> option_type = ql.Option.Call
>>> risk_free_rate = 0.175/100
>>> barrier_price = 400.0
>>> barrier_type = ql.Barrier.DownOut
>>> rebate = 0 # no rebate
>>> day_count = ql.Actual365Fixed()
>>> calendar = ql.TARGET()
>>>
>>>
>>> marketpremium = 18.64
>>>
>>> ql.Settings.instance().evaluationDate = calculation_date
>>>
>>> # Constructing the EU option in QL
>>> payoff = ql.PlainVanillaPayoff(option_type, strike_price)
>>> eu_exercise = ql.EuropeanExercise(maturity_date)
>>> barrier_eu_option =
>>> ql.BarrierOption(barrier_type,barrier_price,rebate,payoff, eu_exercise)
>>>
>>> # The Black-Scholes-Merton process is constructed
>>> spot_handle = ql.QuoteHandle(
>>> ql.SimpleQuote(spot_price)
>>> )
>>> flat_rate_ts = ql.YieldTermStructureHandle(
>>> ql.FlatForward(calculation_date,
>>>   risk_free_rate,
>>>   day_count)
>>> )
>>> flat_vol_ts = ql.BlackVolTermStructureHandle(
>>> ql.BlackConstantVol(calculation_date,
>>> calendar,
>>> volatility,
>>> day_count)
>>> )
>>> bs_process = ql.BlackScholesProcess(spot_handle,
>>>   flat_rate_ts,
>>>   flat_vol_ts)
>>>
>>> # compute the theoretical price using the AnalyticEuropeanEngine
>>>
>>> barrier_eu_option.setPricingEngine(ql.FdBlackScholesBarrierEngine(bs_process))
>>> br_eu_price = barrier_eu_option.NPV()
>>> print("The theoretical price for  EU barrier is %lf" % br_eu_price)
>>>
>>> ############################################################################
>>>
>>> Regards,
>>> Ashish
>>>
>>> On Fri, 17 Jun 2022 at 02:40, Jonathan Sweemer <sw...@gm...>
>>> wrote:
>>>
>>>> Hi Ashish, can you share your code or a link to it?
>>>>
>>>> 2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:
>>>>
>>>>> Hi,
>>>>>
>>>>> Any guidance on this error in barriers?
>>>>>
>>>>> Regards,
>>>>> Ashish
>>>>>
>>>>> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
>>>>> wrote:
>>>>>
>>>>>> Dear team,
>>>>>>
>>>>>> I need some guidance on the pricing of the barrier option using
>>>>>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>>>>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>>>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>>>>> this case? Can't the engine handle it?
>>>>>> 2. For Down-in, I am getting the same error whereas the option gets
>>>>>> activated and is still live then why is this not priced by the engine? Am
>>>>>> I doing something wrong?
>>>>>>
>>>>>> What is the difference between FdBlackScholesBarrierEngine
>>>>>> and AnalyticBarrierEngine? I compared a few results and found a slight
>>>>>> difference in values. Any suggestion on which one to use?
>>>>>>
>>>>>> Any help would be appreciated.
>>>>>>
>>>>>> Thanks
>>>>>> Ashish
>>>>>>
>>>>> _______________________________________________
>>>>> QuantLib-users mailing list
>>>>> Qua...@li...
>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>>>
>>>>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

[Ashish B. <ash...@gm...>]

I am not pricing with barrier breach on the inception day.

Assuming the trade started out of barrier limit, i.e. higher than barrier
for DI and DO type, I am pricing during the life of trade. During life the
underlying could move knocking the barrier. The code starts showing error
after the breach.

I am looking to improve code so that during life of trade it shows right
price output instead of error.

Ashish

On Mon, Jun 20, 2022, 4:16 PM Jonathan Sweemer <sw...@gm...> wrote:

> I can reproduce the exception that you're seeing. As for the explanation,
> I'm not an expert in this code, but here's my take on it:
>
> 1. for Down-Out, when I pass the spot less than the barrier, I get
>> RunTimeerror for interpolation range. Should I block the pricing at all in
>> this case? Can't the engine handle it?
>
> Generally you'd expect a down-and-out call to be quoted with spot above
> the barrier, so starting with spot below the barrier is kind of a
> degenerate case, as the product knocks out before it even starts, so I
> believe the exception is warranted (though perhaps not as informative as it
> could be).
>
> 2. For Down-in, I am getting the same error whereas the option gets
>> activated and is still live then why is this not priced by the engine? Am
>> I doing something wrong?
>
> Similar to question 1, you'd expect a down-and-in call to be quoted with
> spot above the barrier, but in this case I agree that a case can be made
> for the product to be live (i.e. knocked-in at inception), although it
> would probably depend on the specific deal terms how to handle this case.
> If you have any such deal terms to refer to, then you might want to submit
> a pull request to handle them accordingly in the code.
>
>
>
> On Mon, Jun 20, 2022 at 5:11 PM Ashish Bansal <ash...@gm...>
> wrote:
>
>> Following is the code that I am using to evaluate the barrier option.
>> Here if I pass the spot which has crossed the barrier for knock-in or
>> knock-out options, I get an error. The down-out is in the code and for
>> down-in, I took the example given in row# 266 of with spot price as 94 from:
>>
>> https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp
>>
>> My python code:
>>
>> ############################################################################
>> import QuantLib as ql
>>
>> # Setting Barrier option parameters
>> calculation_date = ql.Date(1, 6, 2021)
>> maturity_date = ql.Date(25, 11, 2021)
>> spot_price = 577.0
>> strike_price = 770.0
>> volatility = 43.56/100
>> option_type = ql.Option.Call
>> risk_free_rate = 0.175/100
>> barrier_price = 400.0
>> barrier_type = ql.Barrier.DownOut
>> rebate = 0 # no rebate
>> day_count = ql.Actual365Fixed()
>> calendar = ql.TARGET()
>>
>>
>> marketpremium = 18.64
>>
>> ql.Settings.instance().evaluationDate = calculation_date
>>
>> # Constructing the EU option in QL
>> payoff = ql.PlainVanillaPayoff(option_type, strike_price)
>> eu_exercise = ql.EuropeanExercise(maturity_date)
>> barrier_eu_option =
>> ql.BarrierOption(barrier_type,barrier_price,rebate,payoff, eu_exercise)
>>
>> # The Black-Scholes-Merton process is constructed
>> spot_handle = ql.QuoteHandle(
>> ql.SimpleQuote(spot_price)
>> )
>> flat_rate_ts = ql.YieldTermStructureHandle(
>> ql.FlatForward(calculation_date,
>>   risk_free_rate,
>>   day_count)
>> )
>> flat_vol_ts = ql.BlackVolTermStructureHandle(
>> ql.BlackConstantVol(calculation_date,
>> calendar,
>> volatility,
>> day_count)
>> )
>> bs_process = ql.BlackScholesProcess(spot_handle,
>>   flat_rate_ts,
>>   flat_vol_ts)
>>
>> # compute the theoretical price using the AnalyticEuropeanEngine
>>
>> barrier_eu_option.setPricingEngine(ql.FdBlackScholesBarrierEngine(bs_process))
>> br_eu_price = barrier_eu_option.NPV()
>> print("The theoretical price for  EU barrier is %lf" % br_eu_price)
>>
>> ############################################################################
>>
>> Regards,
>> Ashish
>>
>> On Fri, 17 Jun 2022 at 02:40, Jonathan Sweemer <sw...@gm...> wrote:
>>
>>> Hi Ashish, can you share your code or a link to it?
>>>
>>> 2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:
>>>
>>>> Hi,
>>>>
>>>> Any guidance on this error in barriers?
>>>>
>>>> Regards,
>>>> Ashish
>>>>
>>>> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
>>>> wrote:
>>>>
>>>>> Dear team,
>>>>>
>>>>> I need some guidance on the pricing of the barrier option using
>>>>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>>>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>>>> this case? Can't the engine handle it?
>>>>> 2. For Down-in, I am getting the same error whereas the option gets
>>>>> activated and is still live then why is this not priced by the engine? Am
>>>>> I doing something wrong?
>>>>>
>>>>> What is the difference between FdBlackScholesBarrierEngine
>>>>> and AnalyticBarrierEngine? I compared a few results and found a slight
>>>>> difference in values. Any suggestion on which one to use?
>>>>>
>>>>> Any help would be appreciated.
>>>>>
>>>>> Thanks
>>>>> Ashish
>>>>>
>>>> _______________________________________________
>>>> QuantLib-users mailing list
>>>> Qua...@li...
>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>>
>>>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

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

I can reproduce the exception that you're seeing. As for the explanation,
I'm not an expert in this code, but here's my take on it:

1. for Down-Out, when I pass the spot less than the barrier, I get
> RunTimeerror for interpolation range. Should I block the pricing at all in
> this case? Can't the engine handle it?

Generally you'd expect a down-and-out call to be quoted with spot above the
barrier, so starting with spot below the barrier is kind of a degenerate
case, as the product knocks out before it even starts, so I believe the
exception is warranted (though perhaps not as informative as it could be).

2. For Down-in, I am getting the same error whereas the option gets
> activated and is still live then why is this not priced by the engine? Am
> I doing something wrong?

Similar to question 1, you'd expect a down-and-in call to be quoted with
spot above the barrier, but in this case I agree that a case can be made
for the product to be live (i.e. knocked-in at inception), although it
would probably depend on the specific deal terms how to handle this case.
If you have any such deal terms to refer to, then you might want to submit
a pull request to handle them accordingly in the code.



On Mon, Jun 20, 2022 at 5:11 PM Ashish Bansal <ash...@gm...>
wrote:

> Following is the code that I am using to evaluate the barrier option. Here
> if I pass the spot which has crossed the barrier for knock-in or knock-out
> options, I get an error. The down-out is in the code and for down-in, I
> took the example given in row# 266 of with spot price as 94 from:
>
> https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp
>
> My python code:
>
> ############################################################################
> import QuantLib as ql
>
> # Setting Barrier option parameters
> calculation_date = ql.Date(1, 6, 2021)
> maturity_date = ql.Date(25, 11, 2021)
> spot_price = 577.0
> strike_price = 770.0
> volatility = 43.56/100
> option_type = ql.Option.Call
> risk_free_rate = 0.175/100
> barrier_price = 400.0
> barrier_type = ql.Barrier.DownOut
> rebate = 0 # no rebate
> day_count = ql.Actual365Fixed()
> calendar = ql.TARGET()
>
>
> marketpremium = 18.64
>
> ql.Settings.instance().evaluationDate = calculation_date
>
> # Constructing the EU option in QL
> payoff = ql.PlainVanillaPayoff(option_type, strike_price)
> eu_exercise = ql.EuropeanExercise(maturity_date)
> barrier_eu_option =
> ql.BarrierOption(barrier_type,barrier_price,rebate,payoff, eu_exercise)
>
> # The Black-Scholes-Merton process is constructed
> spot_handle = ql.QuoteHandle(
> ql.SimpleQuote(spot_price)
> )
> flat_rate_ts = ql.YieldTermStructureHandle(
> ql.FlatForward(calculation_date,
>   risk_free_rate,
>   day_count)
> )
> flat_vol_ts = ql.BlackVolTermStructureHandle(
> ql.BlackConstantVol(calculation_date,
> calendar,
> volatility,
> day_count)
> )
> bs_process = ql.BlackScholesProcess(spot_handle,
>   flat_rate_ts,
>   flat_vol_ts)
>
> # compute the theoretical price using the AnalyticEuropeanEngine
>
> barrier_eu_option.setPricingEngine(ql.FdBlackScholesBarrierEngine(bs_process))
> br_eu_price = barrier_eu_option.NPV()
> print("The theoretical price for  EU barrier is %lf" % br_eu_price)
>
> ############################################################################
>
> Regards,
> Ashish
>
> On Fri, 17 Jun 2022 at 02:40, Jonathan Sweemer <sw...@gm...> wrote:
>
>> Hi Ashish, can you share your code or a link to it?
>>
>> 2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:
>>
>>> Hi,
>>>
>>> Any guidance on this error in barriers?
>>>
>>> Regards,
>>> Ashish
>>>
>>> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
>>> wrote:
>>>
>>>> Dear team,
>>>>
>>>> I need some guidance on the pricing of the barrier option using
>>>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>>> this case? Can't the engine handle it?
>>>> 2. For Down-in, I am getting the same error whereas the option gets
>>>> activated and is still live then why is this not priced by the engine? Am
>>>> I doing something wrong?
>>>>
>>>> What is the difference between FdBlackScholesBarrierEngine
>>>> and AnalyticBarrierEngine? I compared a few results and found a slight
>>>> difference in values. Any suggestion on which one to use?
>>>>
>>>> Any help would be appreciated.
>>>>
>>>> Thanks
>>>> Ashish
>>>>
>>> _______________________________________________
>>> QuantLib-users mailing list
>>> Qua...@li...
>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>
>>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

[Ashish B. <ash...@gm...>]

Following is the code that I am using to evaluate the barrier option. Here
if I pass the spot which has crossed the barrier for knock-in or knock-out
options, I get an error. The down-out is in the code and for down-in, I
took the example given in row# 266 of with spot price as 94 from:
https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp

My python code:
############################################################################
import QuantLib as ql

# Setting Barrier option parameters
calculation_date = ql.Date(1, 6, 2021)
maturity_date = ql.Date(25, 11, 2021)
spot_price = 577.0
strike_price = 770.0
volatility = 43.56/100
option_type = ql.Option.Call
risk_free_rate = 0.175/100
barrier_price = 400.0
barrier_type = ql.Barrier.DownOut
rebate = 0 # no rebate
day_count = ql.Actual365Fixed()
calendar = ql.TARGET()


marketpremium = 18.64

ql.Settings.instance().evaluationDate = calculation_date

# Constructing the EU option in QL
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
eu_exercise = ql.EuropeanExercise(maturity_date)
barrier_eu_option =
ql.BarrierOption(barrier_type,barrier_price,rebate,payoff, eu_exercise)

# The Black-Scholes-Merton process is constructed
spot_handle = ql.QuoteHandle(
ql.SimpleQuote(spot_price)
)
flat_rate_ts = ql.YieldTermStructureHandle(
ql.FlatForward(calculation_date,
  risk_free_rate,
  day_count)
)
flat_vol_ts = ql.BlackVolTermStructureHandle(
ql.BlackConstantVol(calculation_date,
calendar,
volatility,
day_count)
)
bs_process = ql.BlackScholesProcess(spot_handle,
  flat_rate_ts,
  flat_vol_ts)

# compute the theoretical price using the AnalyticEuropeanEngine
barrier_eu_option.setPricingEngine(ql.FdBlackScholesBarrierEngine(bs_process))
br_eu_price = barrier_eu_option.NPV()
print("The theoretical price for  EU barrier is %lf" % br_eu_price)
############################################################################

Regards,
Ashish

On Fri, 17 Jun 2022 at 02:40, Jonathan Sweemer <sw...@gm...> wrote:

> Hi Ashish, can you share your code or a link to it?
>
> 2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:
>
>> Hi,
>>
>> Any guidance on this error in barriers?
>>
>> Regards,
>> Ashish
>>
>> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
>> wrote:
>>
>>> Dear team,
>>>
>>> I need some guidance on the pricing of the barrier option using
>>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>>> RunTimeerror for interpolation range. Should I block the pricing at all in
>>> this case? Can't the engine handle it?
>>> 2. For Down-in, I am getting the same error whereas the option gets
>>> activated and is still live then why is this not priced by the engine? Am
>>> I doing something wrong?
>>>
>>> What is the difference between FdBlackScholesBarrierEngine
>>> and AnalyticBarrierEngine? I compared a few results and found a slight
>>> difference in values. Any suggestion on which one to use?
>>>
>>> Any help would be appreciated.
>>>
>>> Thanks
>>> Ashish
>>>
>> _______________________________________________
>> QuantLib-users mailing list
>> Qua...@li...
>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>
>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

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

Hi Ashish, can you share your code or a link to it?

2022년 6월 17일 (금) 03:35, Ashish Bansal <ash...@gm...>님이 작성:

> Hi,
>
> Any guidance on this error in barriers?
>
> Regards,
> Ashish
>
> On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...>
> wrote:
>
>> Dear team,
>>
>> I need some guidance on the pricing of the barrier option using
>> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
>> 1. for Down-Out, when I pass the spot less than the barrier, I get
>> RunTimeerror for interpolation range. Should I block the pricing at all in
>> this case? Can't the engine handle it?
>> 2. For Down-in, I am getting the same error whereas the option gets
>> activated and is still live then why is this not priced by the engine? Am
>> I doing something wrong?
>>
>> What is the difference between FdBlackScholesBarrierEngine
>> and AnalyticBarrierEngine? I compared a few results and found a slight
>> difference in values. Any suggestion on which one to use?
>>
>> Any help would be appreciated.
>>
>> Thanks
>> Ashish
>>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>

Re: [Quantlib-users] Queries on the impact of barrier hit on Barrier pricing

[Ashish B. <ash...@gm...>]

Hi,

Any guidance on this error in barriers?

Regards,
Ashish

On Tue, 14 Jun 2022 at 13:03, Ashish Bansal <ash...@gm...> wrote:

> Dear team,
>
> I need some guidance on the pricing of the barrier option using
> the FdBlackScholesBarrierEngine in the event that the barrier is hit:
> 1. for Down-Out, when I pass the spot less than the barrier, I get
> RunTimeerror for interpolation range. Should I block the pricing at all in
> this case? Can't the engine handle it?
> 2. For Down-in, I am getting the same error whereas the option gets
> activated and is still live then why is this not priced by the engine? Am
> I doing something wrong?
>
> What is the difference between FdBlackScholesBarrierEngine
> and AnalyticBarrierEngine? I compared a few results and found a slight
> difference in values. Any suggestion on which one to use?
>
> Any help would be appreciated.
>
> Thanks
> Ashish
>

Re: [Quantlib-users] Callable Bonds

[El Y. F. <fah...@gm...>]

I have another question for you,
I am using a platform to compare results and sometimes it gives negative
effective convexity which I can't replicate in Quantlib for and European
callable fixed rate bond and I don't know why even if the other output
matchs like OAS, YTM and eff duration.

Le jeu. 16 juin 2022 à 12:44, El Yousfi Fahd <fah...@gm...> a
écrit :

> Hi,
> i am working on a library for pricing callable bonds,
> and I have a few questions:
> 1 - for fixed rate callable  bonds, is there a way to know when it is
> called and when?  because I am trying to approximate American calls with
> daily  European calls?
> 2 - doesn quantlib support floating rate callable bonds?
>
> Thank you
>