Re: [Quantlib-users] SWIG/examples/isda-cds.py

[Francis D. <fdf...@gm...>]

Hi Ashwani,

I had a quick look at this. The attached Python file uses the data and code
that you have provided, with minor amendments described below, to get
within $5 of the upfront vs. the ISDA CDS model output. The attached Excel
sheet uses the ISDA model add-in available here
<https://www.cdsmodel.com/cdsmodel/documentation.html#> to generate the
ISDA CDS model upfront values that I am using for comparison. The code also
checks the discount factors vs. the ISDA CDS model output.

The minor amendments were:

   - 1Y IR instrument is a depo as opposed to a swap
   - The piecewise yield curve must be built with a reference date equal to
   spot date i.e. trade date (31 Mar 2021 + 2) = 2 Apr 2021. I had to copy
   that curve and insert a discount factor of 1.0 on 31 Mar 2021. Without
   this, the code generates an error yield term structure reference date
   (April 2nd, 2021 should be evaluation date (March 31st, 2021). This is
   coming from here
   <https://github.com/lballabio/QuantLib/blob/master/ql/pricingengines/credit/isdacdsengine.cpp#L84>
and
   could probably be relaxed.
   - Post CDS Big Bang, protection is effective immediately on the trade
   date so you don't need trade date + 1 when creating the CDS instrument.
   This is an ISDA CDS model implementation detail, i.e. current period break
   up into Riskless days and Risky days, and is taken care of in the engine
   here
   <https://github.com/lballabio/QuantLib/blob/master/ql/pricingengines/credit/isdacdsengine.cpp#L100>
   .

The output is as follows, where I have set the tolerance to $5:
2021-05-14 16:12:59 INFO     Check Markit values
2021-05-14 16:12:59 INFO
0.168958,-1005800.972539,-1005801.000000,0.027461,True
2021-05-14 16:12:59 INFO
0.168945,-1685138.710635,-1685141.000000,2.289365,True
2021-05-14 16:12:59 INFO
0.168904,-2255634.376402,-2255639.000000,4.623598,True
2021-05-14 16:12:59 INFO
0.168860,-2729114.272363,-2729119.000000,4.727637,True
2021-05-14 16:12:59 INFO
0.168821,-3121225.248050,-3121230.000000,4.751950,True
2021-05-14 16:12:59 INFO     distance: 16.420010

I don't have access to Bloomberg to check what it generates for a trade
date of 31 Mar 2021 (as an aside, the IR values that you provided appear to
be from the 31 Mar 2021 ISDA IR file whereas for a trade date of 31 Mar
2021 I think it is convention to use the file for 30 Mar 2021 - you will
see effectiveasof equal to 2021-03-31 in that file). The values that you
have provided from Bloomberg look closer to the ISDA CDS converter values
without accrued rebate but they are still quite a distance off those also.

Thanks,
Francis.

On Fri, May 14, 2021 at 2:29 PM Luigi Ballabio <lui...@gm...>
wrote:

> Ashwani,
>     the Python example doesn't get results within tolerance either, so I
> doubt there's an obvious answer here.  We should probably check the code of
> the engine.
>
> Luigi
>
>
> On Thu, Apr 1, 2021 at 1:41 AM Ashwani Singh <ash...@gm...>
> wrote:
>
>> Using isda-cds.py
>> <https://github.com/lballabio/QuantLib-SWIG/blob/master/Python/examples/isda-engine.py>
>> example to compare with Bloomberg ISDA Standard Upfront Model but the
>> difference between QL fairUpfront and that from BBG  is much larger than
>> the precision set. Running just for 40% recovery and 1000 bps case while
>> looping through 5 maturities (1Y-5Y).
>>
>> Results (as below):
>>
>>    Hazard   Upfront  Market Value  Distance  Within tolerance
>> 0    0.17 -1.00e+06      -1003010    554.96             False
>> 1    0.17 -1.68e+06      -1682459    641.48             False
>> 2    0.17 -2.25e+06      -2253350    426.70             False
>> 3    0.17 -2.73e+06      -2727484     62.97             False
>> 4    0.17 -3.12e+06      -3120142    458.70             False
>> total distance: 2144.81
>>
>>
>> Anyone who might have tried this, could you please point the obvious
>> mistake I am committing here? Code attached.
>>
>> Thanks.
>>
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