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Résumé de communication

Résumé 264 :

Statistical Estimation of Optimal Portfolios for Dependent Returns
Taniguchi, Masanobu
Waseda University

In this talk, we discusses the asymptotic efficiency of estimators for optimal portfolios when returns are vector-valued non-Gaussian stationary processes. We give the asymptotic distribution of portfolio estimators ^g for non-Gaussian dependent return processes. Next we address the problem of asymptotic efficiency for the class of estimators ^g. First, it is shown that there are some cases when the asymptotic variance of ^g under non-Gaussianity can be smaller than that under Gaussianity. The result shows that non-Gaussianity of the returns does not always affect the efficiency badly. Second, we give a necessary and sufficient condition for ^g to be asymptotically efficient when the return process is Gaussian, which shows that ^g is not asymptotically efficient generally. From this point of view we propose to use maximum likelihood type estimators for g, which are asymptotically efficient. Also we report that the results are extended to the case when the return processes are locally stationary.