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• W3143549726 abstract "This paper proposes a conjugate Bayesian regression model to estimate the covariance matrix of a large number of securities. Characterizing the return generating process with an unrestricted factor model, prior beliefs impose structure while preserving estimator consistency. This framework accommodates economically-motivated prior beliefs and nests shrinkage covariance matrix estimators, providing a common model for their interpretation. Minimizing posterior finite-sample square error delivers a fully-automated covariance matrix estimator with beliefs that become diffuse as the sample grows relative to the dimension of the problem. In application, this Stein-optimal posterior covariance matrix performs well in a large set of simulation experiments. ∗Division of the Humanities and Social Sciences; MC 228-77; California Institute of Technology; Pasadena, CA 91125; bgillen@caltech.edu; tel: (626) 395-4061; fax: (626) 405-9841; This paper is taken from the third chapter of my doctoral thesis at the University of California, San Diego. I am grateful to Ayelen Banegas, Christian Brownlees, Gray Calhoun, Khai Chiong, Michael Ewens, Harry Markowitz, Alberto Rossi, Allan Timmermann, Michael Wolf, and Rossen Valkanov as well as participants in seminars at UC San Diego, UC Irvine, and the First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance for helpful comments and discussion." @default.
• W3143549726 created "2021-04-13" @default.
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• W3143549726 date "2014-01-01" @default.
• W3143549726 modified "2023-09-27" @default.
• W3143549726 title "An Empirical Bayesian Approach to Stein-Optimal Covariance Matrix Estimation∗" @default.
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