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5

"It's compliated" because the trading strategy performance will depend on the data which is most likely serially correlated. So you want to look into bootstrap approaches for time series such as the block bootstrap, or the wild bootstrap. Another approach would be to look into 'random portfolios' or an approximation thereof. The basic idea is to test how ...


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R package TTR has rolling window algorithms and understands day counting etc. It stands on the shoulders of xts (which extends zoo) and quantmod


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It obviously depends on what you're trying to do but since we're speaking about returns zero centering is what's usually done because of the null hypothesis claiming that expected excess returns are zero. You zero center the distribution because you want to obtain a distribution satisfying the null hypothesis. In this distribution you then plug your sample ...


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The correct procedure for parametric bootstrap is: 1) fit the data with a distribution of the parametric family (normal, Student's t, etc.; you should choose the one that fits the data in the best way, using some criteria to choose, such as Akaike Information Criteria or others); 2) draw n random samples from the fitted distribution, and estimate the ...


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I am not sure you have the same definition of bootstrap than myself: bootstrap is mainly a way to estimate the variance of estimators when you do not have a closed form formula to obtain it directly (thanks to Efron's theorem). It means if you want the variance of your estimator of returns or covariance, you could use bootstrapping. Bad news: if you ...



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