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ISEM学术论坛[108]Shrinkage Estimation of Covariance Matrix for Portfolio Choice with High Frequency Data

题目:Shrinkage Estimation of Covariance Matrix for Portfolio Choice with High Frequency Data
报告人: Cheng Liu (Associate Professor ,  Wuhan University)
报告时间:2017年10月26日(周四) 15:30
地点:国际经管学院会议室(诚明楼三层)
主办方:国际经济管理学院


摘要: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 relatively 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 observations 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 → ∞. The advantages of our proposed estimator are demonstrated using real data.

 

报告人简介:Cheng LIU is an associate professor at Wuhan University. He works on Big Data analysis, Missing data analysis, and Financial Econometrics. For more information, please see his website at https://chengliu-whu.weebly.com/

(日期:2017-10-26 作者: 来源:)