Re: [Quantlib-users] how to calibrate GSR model based on european swaption vol surface

[Daniel <gr...@gm...>]

Thank you Dmitri for your information. Actually I increased the vol surface
number to more than to be calibration volatility number, now I can solve
for mean reversion and volatility now.

But  experience another error when I tried to change the yield curve and
volatility surface to some other type, I got below error.

---> 14 model.calibrate(swaptions, optimization_method, end_criteria)

~\.conda\envs\tf-gpu\lib\site-packages\QuantLib\QuantLib.py in
calibrate(self, *args)          def calibrate(self, *args):>
return _QuantLib.Gsr_calibrate(self, *args)          def
setParams(self, params):
RuntimeError: G(t,w) should be called with (t,w)=(29.9985,60.0055) in
Range [0,60].


What does this runtimeError mean?


Thanks,

Mark






On Mon, Aug 22, 2022 at 12:50 PM Dmitri Goloubentsev <dm...@ma...>
wrote:

> Hi Mark,
>
>
> The error is coming from LM optimiser. You should reduce number of points
> in your vol surface or add more calibration instruments or add some sort of
> regularisation.
>
> Kind regards,
> Dmitri.
>
> On Mon, 22 Aug 2022, 17:26 Daniel, <gr...@gm...> wrote:
>
>> All,
>>
>> Please forgive me if someone has answered a similar question before.
>>
>> I am trying to calibrate interest rate vol surface using GSR (gaussian
>> short rate model) based on this post
>>
>> http://gouthamanbalaraman.com/blog/short-interest-rate-model-calibration-quantlib.html
>>
>> and I found a python example for GSR model calibration.
>> https://github.com/mlungwitz/notebooks/blob/master/GSR_Example.ipynb
>>
>> What I tried to do is to calibrate the same european swaption vol surface
>> in the 1st python example based on 2nd example gsr model specification,
>> what I added is:
>> gsr  = ql.Gsr(term_structure,stepDates, sigmas,reversions);
>> engine = ql.Gaussian1dSwaptionEngine(gsr, 64, 7.0, True, False,
>> term_structure)
>> swaptions = create_swaption_helpers(data, index, term_structure, engine)
>>
>> optimization_method = ql.LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8)
>> end_criteria = ql.EndCriteria(1000, 100, 1e-6, 1e-8, 1e-8)
>> model.calibrate(swaptions, optimization_method, end_criteria)
>>
>> But this will give me this error:
>>
>> RuntimeError: less functions (5) than available variables (12)
>>
>> Is the error caused by Gaussian1dSwaptionEngine? Should I proceed with Gaussian1dNonstandardSwaptionEngine? What change should I do to make it work ?
>>
>>
>> Thanks,
>>
>> Mark
>>
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>