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The dependence structure in volatility between Shanghai and Shenzhen Stock Market in China: A copula-MEM approach

The dependence structure in volatility between Shanghai and Shenzhen Stock Market in China: A... PurposeThis study aims to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.Design/methodology/approachUsing 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 (ML) estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.FindingsThis 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/valuePrevious 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

The dependence structure in volatility between Shanghai and Shenzhen Stock Market in China: A copula-MEM approach

China Finance Review International , Volume 6 (3) – Aug 15, 2016

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2044-1398
DOI
10.1108/CFRI-09-2015-0122
Publisher site
See Article on Publisher Site

Abstract

PurposeThis study aims to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.Design/methodology/approachUsing 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 (ML) estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.FindingsThis 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/valuePrevious 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.

Journal

China Finance Review InternationalEmerald Publishing

Published: Aug 15, 2016

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