Jump robust daily covariance estimation by disentangling variance and correlation components

Authors:
Kris Boudt;Jonathan Cornelissen;Christophe Croux
Affiliations:
Faculty of Business and Economics, K.U. Leuven, Belgium and Department of Business Studies, Lessius University College, Belgium;Faculty of Business and Economics, K.U. Leuven, Belgium;Faculty of Business and Economics, K.U. Leuven, Belgium
Venue:
Computational Statistics & Data Analysis
Year:
2012

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Abstract

A jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns is proposed. It disentangles covariance estimation into variance and correlation components. This allows us to account for non-synchronous trading by estimating correlations over lower sampling frequencies. The efficiency gain of disentangling covariance estimation and the jump robustness of the estimator are illustrated in a simulation study. In an application to the Dow Jones Industrial Average constituents, it is shown that the proposed estimator leads to more stable portfolios.