Re: [Quantlib-dev] [Quantlib-users] HullWhite model calibration via normal vols

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

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On Tue, Oct 1, 2019, 09:57 Steven Van Haren <sh...@op...> wrote:

> Hi Robin,
>
> You have to use BlackCalibrationHelper. This was renamed a while back.
>
> Kind regards,
> Steven
>
> Op 1 okt. 2019 om 09:50 heeft Robin Schreur <rob...@ho...>
> het volgende geschreven:
>
> Hi All,
>
> I want to calibrate the HullWhite model via normal swaption vols (using
> Jamshidian swaption model). I found the code below which does this (
> https://github.com/lballabio/QuantLib-SWIG/issues/98)
> Unfortunately I get the following error:
>
> AttributeError: 'function' object has no attribute 'RelativePriceError'.
> Looks like ql.Calibrationhelper doesn't have a RelativePriceError
> attribute. What can I use instead of
> ql.CalibrationHelper.RelativePriceError
>
> Many thanks!
>
> Robin
>
>
>
> from collections import namedtuple
> import QuantLib as ql
>
> def create_swaption_helpers(data, index, term_structure, engine):
>     swaptions = []
>     fixed_leg_tenor = ql.Period(1, ql.Years)
>     fixed_leg_daycounter = ql.Actual360()
>     floating_leg_daycounter = ql.Actual360()
>     for d in data:
>         vol_handle = ql.QuoteHandle(ql.SimpleQuote(d.volatility))
>         helper = ql.SwaptionHelper(ql.Period(d.start, ql.Years),
>                                    ql.Period(d.length, ql.Years),
>                                    vol_handle,
>                                    index,
>                                    fixed_leg_tenor,
>                                    fixed_leg_daycounter,
>                                    floating_leg_daycounter,
>                                    term_structure,
>                                    ql.CalibrationHelper.RelativePriceError,
>                                    ql.nullDouble(),
>                                    1.,
>                                    ql.Normal,
>                                    0.
>                                    )
>         helper.setPricingEngine(engine)
>         swaptions.append(helper)
>     return swaptions
>
>
>
> today = ql.Date(15, ql.February, 2002)
> settlement = ql.Date(19, ql.February, 2002)
> ql.Settings.instance().evaluationDate = today
> term_structure = ql.YieldTermStructureHandle(
>     ql.FlatForward(settlement, 0.04875825, ql.Actual365Fixed())
> )
> index = ql.Euribor1Y(term_structure)
> CalibrationData = namedtuple("CalibrationData",
>                              "start, length, volatility")
> data = [CalibrationData(1, 5, 0.1148),
>         CalibrationData(2, 4, 0.1108),
>         CalibrationData(3, 3, 0.1070),
>         CalibrationData(4, 2, 0.1021),
>         CalibrationData(5, 1, 0.1000)]
> model = ql.HullWhite(term_structure)
> engine = ql.JamshidianSwaptionEngine(model)
> 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(10000, 100, 1e-6, 1e-8, 1e-8)
> model.calibrate(swaptions, optimization_method, end_criteria)
>
> a, sigma = model.params()
>
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