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I want to ask if the bootstrap method for asset allocation is preferable. For instance, suppose that we have data for the past returns for two stocks. Is it wise to generate the efficient frontierby estimating the correlations, the returns and the variances via the bootstrap method?

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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 believe your data are iid and Gaussian, you have a closed form formula,
  • if you don't, and especially if you believe in autocorrelations in retruns, bootstrap is the very difficult.

The best reference for bootstrapping non iid series is Giné, E. (1997). Lectures on some aspects of the bootstrap. In E. Giné, G. R. Grimmett, L. S. Coste, and P. Bernard (Eds.), Ecole d'été de Probabilités de Saint-Flour, XXVI, Volume 1665 of Lecture Notes in Math, pp. 37-152. Springer Verlag.

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