Re: [Quantlib-users] [EXT] Re: Forward CMT rates from G2++ calibrated model for OAS model

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

It might not be so bad.  The generation of the paths is driven from Python
but performed by the GaussianPathGenerator class in C++.  I'd give it a try.

Luigi


On Tue, Jan 30, 2024 at 1:28 PM Philippe Hatstadt <
phi...@ex...> wrote:

> Thank you. I looked at it and it’s great. His post-calibration  simulation
> on HW is done in Python though so performance will be an issue. Thence my
> asking if there were routines existing in C++.
>
> Thank you.
>
> Regards
>
> Philippe Hatstadt
> +1-203-252-0408
>
>
> On Jan 30, 2024, at 7:24 AM, Luigi Ballabio <lui...@gm...>
> wrote:
>
> 
> Hi, not much is already existing, I'm afraid.  You can have a look at
> Goutham's post at
> https://gouthamanbalaraman.com/blog/hull-white-simulation-quantlib-python.html
> (also in the cookbook if you have it) for a few ideas; he generates
> interest-rate paths based on a Hull/White model, but something similar
> should work for G2 as well.
>
> Hope this helps,
>     Luigi
>
>
> On Wed, Jan 24, 2024 at 8:03 PM Philippe Hatstadt <
> phi...@ex...> wrote:
>
>> I was able to calibrate a G2++ model to normal UST swaption volatilities
>> (heroically using SOFR swaptions and re-scaling by rates ratio to generate
>> so-called Treasury Swaption vol surface).
>> Code is below:
>>
>> model = G2(term_structure);
>> # engine = TreeSwaptionEngine(model, 25)
>> # engine = ql.G2SwaptionEngine(model, 10, 400)
>> engine = ql.FdG2SwaptionEngine(model)
>> swaptions = create_swaption_helpers_normal(data, index, term_structure,
>> engine)
>> optimization_method = LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8)
>> end_criteria = EndCriteria(1000, 100, 1e-6, 1e-8, 1e-8)
>> model.calibrate(swaptions, optimization_method, end_criteria)
>> a, sigma, b, eta, rho = model.params()\
>>
>> The question now is as follows: I want to use this model towards a GNMA
>> OAS model for which I would need monte-carlo paths of 2y/5y/10y forward CMT
>> rates spaced say monthly.
>>
>> I assume that I would first need to use the 5 G2++ parameters calibrated
>> above and then generate paths of the short rate, then somehow compute
>> forward CMT at each forward monthly epoch Ti by computing the break-even
>> coupon C10(Ti) such that PV(Ti, bond(cpn=C10(Ti)) == 100?
>> Are there existing QL classes or modules that do all that from a given
>> calibrated model like above?
>> By the same token, I would also need stochastic pathwise discount factor
>> vectors DF(Ti, path j), i=0 to 30y monthly. Is there also a QL module that
>> generates those? I can obviously do it manually, but I am on the python
>> side, so I want to re-use as much of existing libraries as I can to use
>> efficient C++ code indirectly via SWIG.
>>
>> Regards
>>
>> Philippe Hatstadt
>>
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>
> 1370 Broadway, Suite 1450 | New York, NY | 10018
>
> [image: https://www.exosfinancial.com/] <https://www.exosfinancial.com/> [image:
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>
> Broker-Dealer services offered through Exos Securities LLC, Member SIPC,
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