Modelling Asymmetric Conditional Dependence between Shanghai and Hong Kong Stock Markets

Journal article


Wu, W, Lau, M and Vigne, S (2017). Modelling Asymmetric Conditional Dependence between Shanghai and Hong Kong Stock Markets. Research in International Business and Finance. 42, pp. 1137-1149. https://doi.org/10.1016/j.ribaf.2017.07.050
AuthorsWu, W, Lau, M and Vigne, S
Abstract

This paper investigates the asymmetric conditional dependence between Shanghai and Hong Kong stock index returns, to assess the impact of the recent financial recession on Chinese equity markets using the Copula approach. We first propose methods for optimal model selection when constructing the conditional margins. The joint conditional distribution is then modeled by the time-varying copula, where the generalised autoregressive score (GAS) model of Creal, et al. (2013) is used to capture the evolution of the copula parameters. Upper and lower parts of the bivariate tail are estimated separately in order to capture the asymmetric property. We find the conditional dependence between the two markets is strongly time-varying. While the correlation decreased before the crisis, it increased significantly prior to 2008, pointing to the existence of contagion between the two markets. Moreover, we find a slightly stronger bivariate upper tail, suggesting the conditional dependence of stock returns is more significantly influenced by positive shocks in China. This finding is further confirmed by a test for asymmetry which shows that the difference between upper and lower joint tails is significant.

Year2017
JournalResearch in International Business and Finance
Journal citation42, pp. 1137-1149
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ribaf.2017.07.050
Web address (URL)https://research.lsbu.ac.uk/do/edit/86y84
Publication dates
Print23 Jul 2017
Publication process dates
Deposited16 Oct 2017
Accepted03 Jul 2017
Accepted author manuscript
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/86y84

Download files


Accepted author manuscript
china stock market_version2_marco.docx
License: CC BY-NC-ND 4.0
File access level: Open

  • 109
    total views
  • 91
    total downloads
  • 0
    views this month
  • 2
    downloads this month

Export as

Related outputs

Stock Market and Inequality Distributions – Evidence from the BRICS and G7 Countries
Dang, D., Wu, W. and Korkos, I. (2024). Stock Market and Inequality Distributions – Evidence from the BRICS and G7 Countries. International Review of Economics & Finance. 92, pp. 1172-1190. https://doi.org/10.1016/j.iref.2024.02.067
The effects of earnings management on information asymmetry and stock price synchronicity
Dang, Q., Korkos, I. and Wu, W. (2023). The effects of earnings management on information asymmetry and stock price synchronicity. Cogent Economics & Finance. 11 (2). https://doi.org/10.1080/23322039.2023.2290359
Quantile Dependence between the Stock, Bond and Foreign Exchange Markets - Evidence from the UK
Hamid, R and Wu, W (2018). Quantile Dependence between the Stock, Bond and Foreign Exchange Markets - Evidence from the UK. Quarterly Review of Economics and Finance. 69, pp. 286-296. https://doi.org/10.1016/j.qref.2018.03.009
The Dependence Structure in Credit Risk between Money and Derivatives Markets: A Time-Varying Conditional Copula Approach
Wu, W. and McMillan. D. (2014). The Dependence Structure in Credit Risk between Money and Derivatives Markets: A Time-Varying Conditional Copula Approach. Managerial Finance. 40 (8), pp. 758-769. https://doi.org/10.1108/MF-07-2013-0184
Non-Parametric Estimation of Copula Parameters: Testing for Time-Varying Correlation
Gong, J., Wu, W., McMillan. D and Shi, D. (2014). Non-Parametric Estimation of Copula Parameters: Testing for Time-Varying Correlation. Studies in Nonlinear Dynamics & Econometrics. 19 (1), pp. 93-106. https://doi.org/10.1515/snde-2012-0089
Dynamic Linkages in Credit Risk: Modeling the Time-Varying Correlation between the Money and Derivatives Markets over the Crisis Period
Wu, W. and McMillan, D. (2013). Dynamic Linkages in Credit Risk: Modeling the Time-Varying Correlation between the Money and Derivatives Markets over the Crisis Period. Journal of Risk. 16 (2), p. 51–59. https://doi.org/10.21314/JOR.2013.270