The dependence structure in volatility between Shanghai and Shenzhen stock market in China: A copula-MEM approach
Abstract
Purpose
The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.
Design/methodology/approach
Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.
Findings
This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view.
Originality/value
Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.
Keywords
Acknowledgements
The authors gratefully acknowledge financial support from National Natural Science Foundation of China (No. 70901055), National Social Science Foundation of China (No. 14CTJ012), and Independent Innovation Fund Project of Tianjin University, China.
Citation
Guo, M. and Wang, X. (2016), "The dependence structure in volatility between Shanghai and Shenzhen stock market in China: A copula-MEM approach", China Finance Review International, Vol. 6 No. 3, pp. 264-283. https://doi.org/10.1108/CFRI-09-2015-0122
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited