Re: [Quantlib-users] greeks with swaptionvolcube2

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

Hi Peter. That’s correct - the delta (and vega,gamma, etc) all smoothen out to normal when I zero out the skew in the volcube.

Btw, the jags are still present when using ’Naive’ mode in the GSR calibration.

Moreover, what’s more relevant is that they are also present even when just using a simple BachelierSwaptionEngine for the vanilla swaption case so I don’t think it’s model related.

FYI, I’m using normal vols throughout (with skew definition essentially a simple +5 normal vols for each 25bp otm - both for payers and recs)

In any case, to answer your question reg gsr calibration, I think I'm doing both (a) and (b) - essentially I’ve added a modified NPV attribute to the below-mentioned swaption class. So it’s a simple 'bump up,calc NPV,bump down, calc NPV, take difference’ using this NPV attribute. Maybe i’m missing one of the steps (a) or (b) in this code?

def NPV(self):
    engine = ql.Gaussian1dSwaptionEngine(self.model, 64, 7.0, True, False, self.discount_curve)
    basket = self.nsswaption.calibrationBasket(self.swapbase, self.swvolcube, 'MaturityStrikeByDeltaGamma')
    for basket_i in basket:
        ql.as_black_helper(basket_i).setPricingEngine(engine)
    method = ql.LevenbergMarquardt()
    ec = ql.EndCriteria(1000, 10, 1e-8, 1e-8, 1e-8)
    self.model.calibrateVolatilitiesIterative(basket, method, ec)
    npv = self.nsswaption.NPV()*self.position
    return npv



> On Jun 8, 2020, at 7:21 PM, Peter Caspers <pca...@gm...> wrote:
> 
> Hi Aleksis,
> 
> do you get the jags only when using a smile, i.e. does the delta get smoother when you use an ATM matrix only?
> 
> Another question I have is a) whether you recalibrate the GSR model after each bump and b) whether you recalculate the calibration basket after each bump as well?
> 
> Thanks
> Peter
> 
>> On 08 Jun 2020, at 17:31, Aleksis Ali Raza via QuantLib-users <qua...@li... <mailto:qua...@li...>> wrote:
>> 
>> Sure, essentially it’s run off this class:
>> 
>> class bermudanswaption():
>> 
>>     def __init__(self, calendar,settlement, used_model, swap, ratecurves, index, swvolcube_clean, swapbase,
>>                  mean_reversion,position):
>> 
>>         discount_curve = ratecurves.loc['discountcurve', 'ratecurves']
>>         self.swvolcube = swvolcube_clean
>>         self.swapbase = swapbase
>>         self.used_model = used_model
>>         self.discount_curve = discount_curve
>>         self.position=position
>> 
>>         fixed_schedule=swap.fixedSchedule()
>>         exerciseDates = [calendar.advance(i, -ql.Period('2D')) for i in fixed_schedule][1:-1]
>>         exercise = ql.BermudanExercise(exerciseDates)
>>         stepDates = exerciseDates
>>         self.exerciseDates=exerciseDates
>>         sigmas = [ql.QuoteHandle(ql.SimpleQuote(0.01))]*(1+len(exerciseDates))
>>         self.used_model = used_model
>> 
>>         if settlement == 'physical':
>>             type = 0
>>             method = 1
>>         else:
>>             type = 1
>>             method = 3
>> 
>>         self.nsswaption = ql.NonstandardSwaption(swap, exercise, type, method)
>>         gsr = ql.Gsr(ratecurves.loc[index, 'ratecurves'],
>>                      stepDates, sigmas, [ql.QuoteHandle(ql.SimpleQuote(mean_reversion))])
>>         engine = ql.Gaussian1dNonstandardSwaptionEngine(gsr, 64, 7.0, True, False,
>>                                                         ql.QuoteHandle(ql.SimpleQuote(0)),
>>                                                         discount_curve, 2)
>>         self.engine = ql.Gaussian1dSwaptionEngine(gsr, 64, 7.0, True, False, discount_curve)
>>         self.nsswaption.setPricingEngine(engine)
>>         self.model = gsr
>> 
>> 
>> 
>> 
>>> On 8 Jun 2020, at 15:50, Christofer Bogaso <bog...@gm... <mailto:bog...@gm...>> wrote:
>>> 
>>> Hi, could you please share your python code? Thanks,
>>> 
>>> On Mon, Jun 8, 2020 at 8:00 PM Aleksis Ali Raza via QuantLib-users <qua...@li... <mailto:qua...@li...>> wrote:
>>> Hi. In case anyone has an explanation: I get a jagged behaviour in a my greeks when I define a (what is to my knowledge sensible) swaption smile using swaptionvolcube2.
>>> 
>>> The greeks are being calculated by source bumping (using a 1bp shift for the rate delta, shown below along with the NPV). The behaviour isn’t too sensitive to the bump size.
>>> 
>>> Portfolio 1 consists of a 1y4y otm payer swaption and Portfolio 2 of a same strike 5y otm bermudan swaption with annual calls (both valued using the Hull-White GSR model calibrated to the swaption vol cube with MaturityStrikeByDeltaGamma mode). 
>>> 
>>> The code is in Python.
>>> 
>>> Thanks, Aleksis
>>>  
>>> 
>>> <optionanalysis_rate.png>
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