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Robust Covariance Matrix Estimation and Portfolio Allocation: The Case of Non-Homogeneous Assets

Abstract : This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. Our study is based on the case of the Maximum Variety Portfolio and may be obviously extended to other classical frameworks with analogous results. We focus on the fact that the assets should preferably be classified in homogeneous groups before applying the proposed methodology which is to whiten the data before estimating the covariance matrix using the robust Tyler M-estimator and the Random Matrix Theory (RMT). The proposed procedure is applied and compared to standard techniques on real market data showing promising improvements.
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https://hal-paris1.archives-ouvertes.fr/hal-02938055
Contributor : Philippe de Peretti <>
Submitted on : Monday, September 14, 2020 - 4:26:45 PM
Last modification on : Thursday, November 19, 2020 - 3:27:08 AM

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Emmanuelle Jay, Thibault Soler, Philippe De Peretti, Christophe Chorro, Ovarez Jean-Philippe. Robust Covariance Matrix Estimation and Portfolio Allocation: The Case of Non-Homogeneous Assets. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain. pp.8449-8453, ⟨10.1109/ICASSP40776.2020.9054100⟩. ⟨hal-02938055⟩

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