> 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 – 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 wrong…
> 
>  
> 
> Thanks for your help
> 
> Best regards
> 
> Eric
> 
> 
> 
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