Shrinkage Estimation of Covariance Matrix for Portfolio Choice with High Frequency Data
Cheng Liu (),
Ningning Xia () and
Jun Yu ()
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Cheng Liu: Economics and Management School of Wuhan University
Ningning Xia: School of Statistics and Management, Shanghai University of Finance and Economics
No 14-2016, Economics and Statistics Working Papers from Singapore Management University, School of Economics
This paper examines the usefulness of high frequency data in estimating the covariance matrix for portfolio choice when the portfolio size is large. A computationally convenient nonlinear shrinkage estimator for the integrated covariance (ICV) matrix of financial assets is developed in two steps. The eigenvectors of the ICV are first constructed from a designed time variation adjusted realized covariance matrix of noise-free log-returns of rel- atively low frequency data. Then the regularized eigenvalues of the ICV are estimated by quasi-maximum likelihood based on high frequency data. The estimator is always positive definite and its inverse is the estimator of the inverse of ICV. It minimizes the limit of the out-of-sample variance of portfolio returns within the class of rotation-equivalent estimators. It works when the number of underlying assets is larger than the number of time series ob- servations in each asset and when the asset price follows a general stochastic process. Our theoretical results are derived under the assumption that the number of assets (p) and the sample size (n) satisfy p/n -> y > 0 as n -> 8. The advantages of our proposed estimator are demonstrated using real data.
Keywords: Portfolio Choice; High Frequency Data; Integrated Covariance Matrix; Shrinkage Function (search for similar items in EconPapers)
JEL-codes: C13 C22 C51 G12 G14 (search for similar items in EconPapers)
Pages: 34 pages
New Economics Papers: this item is included in nep-ecm, nep-mst, nep-ore and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2016_014
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