Jump Robust Daily Covariance Estimation by Disentangling Variance and Correlation Components

We propose a jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate correlations over lower sampling frequencies, to account for non-synchronous trading. 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, we show that the proposed estimator leads to more stable portfolios

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Year of publication:	2012
Authors:	Boudt, Kris
Other Persons:	Cornelissen, Jonathan (contributor) ; Croux, Christophe (contributor)
Publisher:	[2012]: [S.l.] : SSRN
Subject:	Korrelation \| Correlation \| Schätztheorie \| Estimation theory \| Varianzanalyse \| Analysis of variance \| Volatilität \| Volatility \| Robustes Verfahren \| Robust statistics \| Kapitaleinkommen \| Capital income \| Schätzung \| Estimation \| ARCH-Modell \| ARCH model

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