Re: [Quantlib-users] Source bumping calculation time

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

Hi Aleksis,

computing the calibration basket via 'MaturityStrikeByDeltaGamma' is
quite time consuming, so I'd try to compute the basket only once
(under the base scenario) and reuse it for each sensitivity bump
scenario.

Thanks
Peter

On Sun, 17 Jan 2021 at 13:52, Aleksis Ali Raza via QuantLib-users
<qua...@li...> wrote:
>
> Hi, a question on optimizing calculation time: the code I’ve written for bermudan swaption valuation in python (see below) takes forever when I run risks using source bumping (bucketed ATM and skew swaption vega are the really killers). I assume it’s the recalibration step I have added for the NPV attribute that’s the issue but I can’t figure a way around that. Any optimization tips would be appreciated (eg. different parameters for a less time-consuming integration scheme??).
>
> Thanks, Aleksis
>
>
> class bermudanswaption():
> 648
> 649     def __init__(self, calendar,settlement, used_model, swap, ratecurves, index, swvolcube_clean, swapbase,
> 650                  mean_reversion,position,NC_periods):
> 651
> 652         discount_curve = ratecurves.loc['discountcurve', 'ratecurves']
> 653         self.swvolcube = swvolcube_clean
> 654         self.swapbase = swapbase
> 655         self.used_model = used_model
> 656         self.discount_curve = discount_curve
> 657         self.position=position
> 658
> 659         fixed_schedule=swap.fixedSchedule()
> 660         exerciseDates = [calendar.advance(i, -ql.Period('2D')) for i in fixed_schedule][1+NC_periods:-1]
> 661         exercise = ql.BermudanExercise(exerciseDates)
> 662         stepDates = exerciseDates
> 663         self.exerciseDates=exerciseDates
> 664         sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]*(1+len(exerciseDates))
> 665         self.used_model = used_model
> 666
> 667         if settlement == 'physical':
> 668             type = 0
> 669             method = 1
> 670         else:
> 671             type = 1
> 672             method = 2
> 673
> 674         self.nsswaption = ql.NonstandardSwaption(swap, exercise, type, method)
> 675         gsr = ql.Gsr(ratecurves.loc[index, 'ratecurves'],
> 676                      stepDates, sigmas, [ql.QuoteHandle(ql.SimpleQuote(mean_reversion))])
> 677         engine = ql.Gaussian1dNonstandardSwaptionEngine(gsr, 64, 7.0, True, False,
> 678                                                         ql.QuoteHandle(ql.SimpleQuote(0)),
> 679                                                         discount_curve, 2)
> 680         self.engine = ql.Gaussian1dSwaptionEngine(gsr, 64, 7.0, True, False, discount_curve)
> 681         self.nsswaption.setPricingEngine(engine)
> 682         self.model = gsr
> 683     def NPV(self):
> 684         engine = ql.Gaussian1dSwaptionEngine(self.model, 64, 7.0, True, False, self.discount_curve)
> 685         basket = self.nsswaption.calibrationBasket(self.swapbase, self.swvolcube, 'MaturityStrikeByDeltaGamma')
> 686         for basket_i in basket:
> 687             ql.as_black_helper(basket_i).setPricingEngine(engine)
> 688         method = ql.LevenbergMarquardt()
> 689         ec = ql.EndCriteria(1000, 10, 1e-8, 1e-8, 1e-8)
> 690         self.model.calibrateVolatilitiesIterative(basket, method, ec)
> 691         npv = self.nsswaption.NPV()*self.position
>
> _______________________________________________
> QuantLib-users mailing list
> Qua...@li...
> https://lists.sourceforge.net/lists/listinfo/quantlib-users