Using, taming or avoiding the factor zoo A double shrinkage estimator for covariance matrices

发布时间：2023/2/22

作者：   Gianluca De Nard 赵钊

单位：   苏黎世大学经济系 华中科技大学经济学院

期刊：   Journal of Empirical Finance, 72, 2023年

Abstract

Existing factor models struggle to model the covariance matrix for a large number of stocks and factors. Therefore, we introduce a new covariance matrix estimator that first shrinks the factor model coefficients and then applies nonlinear shrinkage to the residuals and factors. The estimator blends a regularized factor structure with conditional heteroskedasticity of residuals and factors and displays superior all-around performance against various competitors. We show that for the proposed double-shrinkage estimator, it is enough to use only the market factor or the most important latent factor(s). Thus there is no need for laboriously taking into account the factor zoo.

ISSN号：1879-1727

链接：Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices - ScienceDirect