[Quantlib-users] Source bumping calculation time

[Aleksis A. R. <ale...@go...>]

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