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

Emmanuelle Jay (), Thibault Soler (), J.-P. Ovarlez (), Philippe de Peretti () and C. Chorro ()
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Emmanuelle Jay: Quanted and Europlace Institute of Finance, Fideas Capital
Thibault Soler: Fideas Capital, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
J.-P. Ovarlez: SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay - ONERA - CentraleSupélec - Université Paris-Saclay, DEMR, ONERA, Université Paris Saclay [Palaiseau] - ONERA - Université Paris-Saclay
Philippe de Peretti: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne
C. Chorro: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Date: 2021-02
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Published in IEEE International Conference on Acoustics Speech and Signal Processing, 2021, 143, pp.8449-8453. ⟨10.1109/ICASSP40776.2020.9054100⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:hal-05106383

DOI: 10.1109/ICASSP40776.2020.9054100

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