Re: [Quantlib-dev] FW: SABR regression

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

oops. For me the test suite in the current master both with and without
#170 runs fine. Only the new regression case included in 170 fails without
the patch. Let me know what error pops up on your side.
Peter



On 18 November 2014 21:57, Ferdinando M. Ametrano <na...@am...>
wrote:

> wow, this was fast!
> btw by "sabr test" I meant one test in the QuantlIb test suite
>
> On Tue, Nov 18, 2014 at 9:47 PM, Peter Caspers <pca...@gm...>
> wrote:
>
>> Alright, I added a test case for the regression you found and one for the
>> direct/inverse functions (a deterministic one, yes). I'll have a look at
>> your data and see if we can extract more interesting test cases from that.
>>
>> And please send the new case when you are back.
>>
>> Peter
>>
>> On 18 November 2014 20:31, Ferdinando M. Ametrano <na...@am...>
>> wrote:
>>
>>> Yes Peter, I can provide a caplet snapshot for a test.
>>>
>>> As for a direct/inverse test: yes please, but avoid random numbers as
>>> that would make tests unpredictable.
>>>
>>> Please also note that one sabr test is currently failing: I don't know
>>> if because of yesterday's patch or it has been failing for a while.
>>> thurdays, back at my desk,  I can provide more details, but you might want
>>> to check it up on your setup in the meantime
>>> On Nov 18, 2014 9:33 AM, "Peter Caspers" <pca...@gm...> wrote:
>>>
>>>> yes, please send an example (otherwise I could already take the one
>>>> from the excel you sent earlier ? or was that made up ?). In addtion I
>>>> think I should add a technical test like
>>>>
>>>> for random input x
>>>> - y = direct(x) generates admissable values
>>>> - direct( inverse( y ) ) = y
>>>>
>>>> for sabr, noarbsabr, zabr and svi. What do you think ?
>>>>
>>>> Peter
>>>>
>>>>
>>>> On 17 November 2014 20:07, Ferdinando M. Ametrano <na...@am...>
>>>> wrote:
>>>>
>>>>> Thank you Peter for the timely fix.
>>>>>
>>>>> One takeaway is to create a unit test to check for possible future
>>>>> regressions.
>>>>> Do you have a relevant dataset from some paper to suggest? If not, I
>>>>> could provide a snapshot of current euro caplets.
>>>>> On Nov 17, 2014 7:40 PM, "MAZZOCCHI PAOLO" <
>>>>> pao...@es...> wrote:
>>>>>
>>>>>>  Hi Peter,
>>>>>>
>>>>>> I tested your modifications and they work properly. The results are
>>>>>> in line with the ones obtained using QL 1.2.
>>>>>>
>>>>>> Anyway, the details (forward and time to expiry) are written in the
>>>>>> excel file attached in the previous email.
>>>>>>
>>>>>>
>>>>>>
>>>>>> time
>>>>>>
>>>>>> fwd
>>>>>>
>>>>>> 0.1342
>>>>>>
>>>>>> 1.1075%
>>>>>>
>>>>>> 0.3833
>>>>>>
>>>>>> 1.1025%
>>>>>>
>>>>>>
>>>>>>
>>>>>> Thank you very much for your help.
>>>>>>
>>>>>>
>>>>>>
>>>>>> Paolo
>>>>>>
>>>>>>
>>>>>>
>>>>>> *Paolo Mazzocchi*
>>>>>>
>>>>>> *Deloitte Consulting Srl*
>>>>>>
>>>>>> consulente di
>>>>>>
>>>>>> *FINANCIAL ENGINEERING - Banca IMI*
>>>>>>
>>>>>> Tel: 02-72615029  Int: 35029
>>>>>>
>>>>>> *pao...@es... <pao...@es...>*
>>>>>>
>>>>>>
>>>>>>
>>>>>> *From:* Peter Caspers [mailto:pca...@gm...]
>>>>>> *Sent:* Monday, November 17, 2014 7:00 PM
>>>>>> *To:* Ferdinando M. Ametrano
>>>>>> *Cc:* maz...@li...; QuantLib Mailing Lists; maddalena zanzi
>>>>>> *Subject:* Re: [Quantlib-dev] FW: SABR regression
>>>>>>
>>>>>>
>>>>>>
>>>>>> Hi Ferdinando,
>>>>>>
>>>>>> I pushed a fix here (effectively it should yield the same results as
>>>>>> the file attached earlier)
>>>>>>
>>>>>> https://github.com/lballabio/quantlib/pull/170
>>>>>>
>>>>>> As far as I remember the reason for the change in the transformation
>>>>>> function (for 1.3 or 1.4 ?) were nan / inf values occuring from time to
>>>>>> time in the calibration. This is why I replaced the parabolas by truncated
>>>>>> parabolas with linear wings (such that the result is C^1).
>>>>>>
>>>>>> If you feel that your test case is still working better in 1.2, could
>>>>>> you please send some more details (forward, time to expiry), I'd be happy
>>>>>> to discuss again then.
>>>>>>
>>>>>>
>>>>>>
>>>>>> Best regards
>>>>>> Peter
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 17 November 2014 13:25, Peter Caspers <pca...@gm...>
>>>>>> wrote:
>>>>>>
>>>>>> Hi Ferdinando,
>>>>>>
>>>>>> one problem is the transformation function direct(...) which is buggy
>>>>>> for alpha and nu. Can you try the attached version instead and see if it
>>>>>> works better ?
>>>>>>
>>>>>> I'd like to have a closer look and try on more cases before I send a
>>>>>> PR though.
>>>>>>
>>>>>> Thanks
>>>>>>
>>>>>> Peter
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 17 November 2014 12:06, Ferdinando M. Ametrano <na...@am...>
>>>>>> wrote:
>>>>>>
>>>>>> Hi Peter
>>>>>>
>>>>>>
>>>>>>
>>>>>> SABR implementation on the current trunk has a regression compared
>>>>>> with QuantLib 1.2.1
>>>>>>
>>>>>>
>>>>>>
>>>>>> While trying to calibrate problematic smiles as:
>>>>>>
>>>>>>
>>>>>>
>>>>>> strike
>>>>>>
>>>>>> 1.00%
>>>>>>
>>>>>> 1.13%
>>>>>>
>>>>>> 1.25%
>>>>>>
>>>>>> 1.38%
>>>>>>
>>>>>> 1.50%
>>>>>>
>>>>>> vol1
>>>>>>
>>>>>> 23.20%
>>>>>>
>>>>>> 20.25%
>>>>>>
>>>>>> 31.21%
>>>>>>
>>>>>> 39.02%
>>>>>>
>>>>>> 50.45%
>>>>>>
>>>>>> vol2
>>>>>>
>>>>>> 16.67%
>>>>>>
>>>>>> 20.20%
>>>>>>
>>>>>> 27.85%
>>>>>>
>>>>>> 32.79%
>>>>>>
>>>>>> 37.27%
>>>>>>
>>>>>>
>>>>>>
>>>>>> 1.2.1 was able to offer these (non-optimal but decent) solutions:
>>>>>>
>>>>>>
>>>>>>
>>>>>> max error
>>>>>>
>>>>>> rms error
>>>>>>
>>>>>> end criteria
>>>>>>
>>>>>> alpha
>>>>>>
>>>>>> beta
>>>>>>
>>>>>> nu
>>>>>>
>>>>>> rho
>>>>>>
>>>>>> 1.79%
>>>>>>
>>>>>> 0.94%
>>>>>>
>>>>>>  StationaryPoint
>>>>>>
>>>>>> 0.58%
>>>>>>
>>>>>> 25.00%
>>>>>>
>>>>>> 346.03%
>>>>>>
>>>>>> 34.19%
>>>>>>
>>>>>> 0.65%
>>>>>>
>>>>>> 0.43%
>>>>>>
>>>>>> StationaryPoint
>>>>>>
>>>>>> 0.62%
>>>>>>
>>>>>> 25.00%
>>>>>>
>>>>>> 193.97%
>>>>>>
>>>>>> 63.66%
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> the current trunk instead fails for the second smile with error
>>>>>> "qlSABRInterpolationAlpha - nu must be non negative: -124.897 not allowed".
>>>>>>
>>>>>>
>>>>>>
>>>>>> If one tries to just use the four leftmost volatilities to simplify
>>>>>> the problem, both calibrations fail with the same error. This leads me to
>>>>>> believe the problem is about the solver looking for a solution in the
>>>>>> negative nu region. If this is the case it could be solved having a
>>>>>> positive constraint for nu. Do you agree? Could you please look into it?
>>>>>>
>>>>>>
>>>>>>
>>>>>> Please find attached a spreadsheet reproducing the error. We might
>>>>>> even want to add a non regression test to the test suite.
>>>>>>
>>>>>>
>>>>>>
>>>>>> thank you for your help
>>>>>>
>>>>>>
>>>>>>
>>>>>> regards
>>>>>>
>>>>>> F
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Prima di stampare, pensa all'ambiente ** Think about the environment
>>>>>> before printing
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>