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

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

Hmm, I'm not sure.   The only thing that comes to mind is that, given a
path for interest rates, one could create some sort of interest-rate term
structure and then extract CMT rates from it.  It's not something I tried,
though.

Luigi

On Tue, Jan 30, 2024 at 1:39 PM Philippe Hatstadt <
phi...@ex...> wrote:

> Except I need to generate CMT rates along each path for my prepayment
> model. That would have be to be a pure  Python Ioop? How would you then
> advise to calculate CMT rates from a path of short rates with the goal to
> try and use as many wrapped Python calls to underlying C++?
>
> Regards
>
> Philippe Hatstadt
> +1-203-252-0408
>
>
> On Jan 30, 2024, at 7:33 AM, Luigi Ballabio <lui...@gm...>
> wrote:
>
> 
> 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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>>
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>>
>> Broker-Dealer services offered through Exos Securities LLC, Member SIPC,
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>
> 1370 Broadway, Suite 1450 | New York, NY | 10018
>
> [image: https://www.exosfinancial.com/] <https://www.exosfinancial.com/> [image:
> https://www.linkedin.com/company/meetexos/about/]
> <https://www.linkedin.com/company/meetexos/about/>
>
> Broker-Dealer services offered through Exos Securities LLC, Member SIPC,
> FINRA. For important disclosures including Form CRS and Regulation BI click
> here <https://www.exosfinancial.com/general-disclosures>.
>
>
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