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Jump robust daily covariance estimation by disentangling variance and correlation components

Kris Boudt, Jonathan Cornelissen and Christophe Croux

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 2993-3005

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.

Keywords: Epps effect; High-frequency data; Integrated covariance; Jumps; Non-synchronous trading; Realized covariance (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1016/j.csda.2011.05.003

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