Jump robust daily covariance estimation by disentangling variance and correlation components
Jonathan Cornelissen and
Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 2993-3005
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:2993-3005
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