A scalar dynamic conditional correlation model:Structure and estimation

作     者：Hui Wang Jiazhu Pan 

作者机构：School of Finance Central University of Finance and Economics Department of Mathematics and Statistics University of Strathclyde 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2018年第61卷第10期

页      面：1881-1906页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020202[经济学-区域经济学] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China(Grant No.71771224) National Social Science Foundation of China(Grant Nos.14ZDA044 and 15BGJ037) the Program for National Statistics Science Research Plan(Grant No.2016LD02) the Program for Innovation Research in Central University of Finance and Economics 

主　　题：dynamic conditional correlation stationarity ergodicity QMLE consistency asymptotic normality 

摘      要：The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only recently and the asymptotic theory of parameter estimation for the DCC model has not yet to be fully discussed. In this paper, we propose an alternative model, namely the scalar dynamic conditional correlation(SDCC) model. Sufficient and easily-checked conditions for stationarity, geometric ergodicity, andβ-mixing with exponential-decay rates are provided. We then show the strong consistency and asymptotic normality of the quasi-maximum-likelihood estimator(QMLE) of the model parameters under regular *** asymptotic results are illustrated by Monte Carlo experiments. As a real-data example, the proposed SDCC model is applied to analyzing the daily returns of the FSTE(financial times and stock exchange) 100 index and FSTE 100 futures. Our model improves the performance of the DCC model in the sense that the Li-Mc Leod statistic of the SDCC model is much smaller and the hedging efficiency is higher.