[R-SIG-Finance] Back testing

[Christofer Bogaso]

Thanks Brian, the resources are really helpful.

However I am not sure if I fully understood the implementation part of
the MSCI's approach. It basically defines different test-statistics
r.g. Z1, Z2, etc. For Z1, it asserts that under null, the expected
value for Z1 will be zero. I failed to see what distribution would it
take under H0, so that I can complete the significance testing and/or
defining some confidence interval under null.

Ideally, with realised daily PnL and forecasted ES, we will have a
time series of Z1 - if my understanding is perfect. To carry out if
E[Z1] = 0, can I do some t-test or some non-parametric test for
testing mean =0?

I think, this should be valid as only assumption was that PnL has to
be independent, may not be identically distributed. My only concern
is, can I use an ordinary significance table for t-test? I am little
concerned because, testing would be done on Z1's values, which are
calculated values, not the original dataset. So a non-parametric test
may be more appropriate.

Any pointer on above thinking is highly appreciated.

On Wed, Jun 10, 2020 at 5:21 PM leo sea <leosea using outlook.com> wrote:
>
> Hi
> I am also interested in in ES backtesting.
> Good idea
> Thanks
>
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> ________________________________
> From: R-SIG-Finance <r-sig-finance-bounces using r-project.org> on behalf of Christofer Bogaso <bogaso.christofer using gmail.com>
> Sent: Wednesday, June 10, 2020 11:38:57 AM
> To: r-sig-finance using r-project.org <r-sig-finance using r-project.org>
> Subject: [R-SIG-Finance] Back testing
>
> Hi,
>
> I was looking for an idea how banks backtest their models for Expected
> Shortfall. Backtesting VaR is well documented but I failed to get any
> practical idea about backtesting ES.
>
> Any pointer towards the best practice will be really helpful.
>
> Thanks,
>
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