Cholesky GAS models for large time-varying covariance matrices  

作　　者：Tingguo Zheng Shiqi Ye 

机构地区：[1]Department of Statistics and Data Science,School of Economics,Xiamen University,Xiamen,361005,China [2]Wang Yanan Institute for Studies in Economics,Xiamen University,Xiamen,361005,China [3]Paula and Gregory Chow Institute for Studies in Economics,Xiamen University,Xiamen,361005,China

出　　处：《Journal of Management Science and Engineering》2024年第1期115-142,共28页管理科学学报（英文版）

基　　金：The authors would like to acknowledge that this work is supported by the Basic Scientific Center of National Science Foundation of China(Project 71988101);the Humanities and Social Science Fund of Ministry of Education of the People's Republic of China under Grant No.22JJD790050;the National Natural Science Foundation of China,General Program under Grant No.71973110 and No.72373125;the National Natural Science Foundation of China,Key Program under Grant No.72033008;the Fundamental Research Funds for the Central Universities under Grant No.20720191072;the Statistical Science Research Program of China under Grant No.2022LZ37 and No.2022LZ06;the Cultivation Program of Financial Security Collaborative Innovation Center,Southwestern University of Finance and Economics under Grant No.JRXTP202202.

摘　　要：This paper develops a new class of multivariate models for large-dimensional time-varying covariance matrices,called Cholesky generalized autoregressive score(GAS)models,which are based on the Cholesky decomposition of the covariance matrix and assume that the parameters are score-driven.Specifically,two types of score-driven updates are considered:one is closer to the GARCH family,and the other is inspired by the stochastic volatility model.We demonstrate that the models can be estimated equation-wise and are computationally feasible for high-dimensional cases.Moreover,we design an equationwise dynamic model averaging or selection algorithm which simultaneously extracts model and parameter uncertainties,equipped with dynamically estimated model parameters.The simulation results illustrate the superiority of the proposed models.Finally,using a sizeable daily return dataset that includes 124 sectors in the Chinese stock market,two empirical studies with a small sample and a full sample are conducted to verify the advantages of our models.The full sample analysis by a dynamic correlation network documents significant structural changes in the Chinese stock market before and after COVID-19.

关 键 词：Cholesky decomposition GAS Dynamic conditional correlations Dynamic model averaging 

分 类 号：U491[交通运输工程—交通运输规划与管理]