Back testing

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.

Back testing

Thread (11 messages)