> Dear all
>
> I have one question that I struggle to find an answer:
>
> Let`s assume I have 2 timeseries of daily PnL data over 2 years coming from 2
> different trading strategies. I want to find out if strategy A is better than
> strategy B. The problem is that the two series have serial correlations,
> hence I cannot just do a simple t-test.
>
> I tried something like this:
>
> 1.create cumulative timeseries of PnL_A = C_A and of PnL_B = C_B
>
> 2.take the difference of both: C_A â C_B = DiffPnL (to see how the
> difference evolves over time)
>
> 3.do a regression: DiffPnL = beta * time + error (I thought if beta is
> significantly different from 0 than the two time series are different)
>
> 4.estimate beta not with OLS, but with the Newey-West method (HAC estimator)
> -> this corrects statistical tests, standard errors for beta
> heteroskedasticity and autocorrelation
>
> BUT: I read something that the tests are biased when the timeseries are unit
> root non-stationary (which is due to the fact that I take cumulative time
> series)
>
>
>
> I am lost! This should be fairly simple: test if two samples differ if they
> have autocorrelation? Probably my approach above is completely wrongâ¦
>
>
>
> Thanks for your help
>
> Best regards
>
> Eric
>
>
>
> The information in this e-mail is intended only for th...{{dropped:23}}
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