Asia-Pacific Financial Markets

, Volume 16, Issue 3, pp 183–210 | Cite as

Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets



In this paper, we study the extreme dependence between the markets in Hong Kong, Shanghai, Shenzhen, Taiwan and Singapore. The tail dependence coefficient (TDC), which measures how likely financial returns move together in extreme market conditions, is modeled dynamically using the Multivariate Generalized Autoregressive Conditional Heteroscedasticity model with the time-varying correlation matrix of Tse and Tsui (Journal of Business & Economic Statistics, 20(3):351–363, 2002). The time paths of the TDC indicate that Hong Kong stocks had the highest extreme dependence during the Asian financial crisis and their TDCs have followed an increasing trend since 2006. The results in this paper also show that the TDC pattern of Singapore with the other markets is very similar to the TDC pattern of Hong Kong with the other markets. An increasing trend in the extreme dependence between Shanghai A Share Index and Shanghai B Share Index and between the Hang Seng Index and the Hong Kong China Enterprise Index is observed from 2002 to 2007. A substantial rise in the TDC between Shenzhen A Share Index and Shenzhen B Share Index was recorded after the China market reforms in 2005. Our TDC modeling with Asian market data provides evidence that Asian markets are becoming integrated and their extreme co-movements during financial turmoil are becoming stronger.


Dynamic correlation Extreme dependence Multivariate GARCH Model Risk management Tail dependence coefficient 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ang A., Chen J. (2002) Asymmetric correlations of equity portfolios. Journal of Financial Economics 63(3): 443–494. doi:10.1016/S0304-405X(02)00068-5 CrossRefGoogle Scholar
  2. Bollerslev T., Wooldridge J.M. (1992) Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews 11(2): 143–172. doi:10.1080/07474939208800229 CrossRefGoogle Scholar
  3. Embrechts P., McNeil A., Straumann D. (2002) Correlation and dependency in risk management: Properties and pitfalls. In: Dempster M.A.H. (eds) Risk Management: Value at Risk and Beyond. Cambridge University Press, Cambridge, pp 176–223Google Scholar
  4. Engle R.F. (2002) Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics 20(3): 339–350. doi:10.1198/073500102288618487 CrossRefGoogle Scholar
  5. Fernandez V. (2008) Copula-based measures of dependence structure in asset returns. Physica A 387: 3615–3628Google Scholar
  6. Frahm G., Junker M., Schmidt R. (2005) Estimating the tail-dependence coefficient: Properties and pitfalls. Insurance, Mathematics & Economics 37(1): 80–100. doi:10.1016/j.insmatheco.2005.05.008 CrossRefGoogle Scholar
  7. Joe H. (1997) Multivariate models and dependence concepts. Chapman & Hall, LondonGoogle Scholar
  8. Jondeau E., Rockinger M. (2006) The copula-GARCH model of conditional dependencies: An international stock market application. Journal of International Money and Finance 25: 827–853. doi:10.1016/j.jimonfin.2006.04.007 CrossRefGoogle Scholar
  9. Longin F., Solnik B. (2001) Correlation structure of international equity markets during extremely volatile periods. The Journal of Finance 56(2): 649–676. doi:10.1111/0022-1082.00340 CrossRefGoogle Scholar
  10. Markowitz H.M. (1952) Portfolio selection. The Journal of Finance 7(1): 77–91. doi:10.2307/2975974 CrossRefGoogle Scholar
  11. McNeil A.J., Frey R., Embrechts P. (2005) Quantitative risk management: concepts, techniques and tools. Princeton University Press, Princeton and OxfordGoogle Scholar
  12. Poon S.H., Rockinger M., Tawn J. (2004) Extreme value dependence in financial markets: diagnostics, models, and financial applications. Review of Financial Studies 17: 581–610. doi:10.1093/rfs/hhg058 CrossRefGoogle Scholar
  13. Schmidt R. (2003) Tail dependence. Statistical tools in finance and insurance. Springer Verlag, New York, pp 65–91Google Scholar
  14. Sharpe W.F. (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance 19(3): 425–442. doi:10.2307/2977928 CrossRefGoogle Scholar
  15. Tse Y.K., Tsui A. (2002) A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics 20(3): 351–363. doi:10.1198/073500102288618496 CrossRefGoogle Scholar
  16. Wei W.W.S. (2005) Time series analysis: univariate and multivariate methods. Pearson Addison Wesley, BostonGoogle Scholar
  17. Zhang Z., Shinki K. (2007) Extreme co-movements and extreme impacts in high frequency data in finance. Journal of Banking & Finance 31: 1399–1415. doi:10.1016/j.jbankfin.2006.10.019 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  1. 1.Department of Information Systems, Business Statistics and Operations Management, School of Business and ManagementThe Hong Kong University of Science and TechnologyClear Water BayHong Kong

Personalised recommendations