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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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
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
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 176–223
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
Fernandez V. (2008) Copula-based measures of dependence structure in asset returns. Physica A 387: 3615–3628
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
Joe H. (1997) Multivariate models and dependence concepts. Chapman & Hall, London
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
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
Markowitz H.M. (1952) Portfolio selection. The Journal of Finance 7(1): 77–91. doi:10.2307/2975974
McNeil A.J., Frey R., Embrechts P. (2005) Quantitative risk management: concepts, techniques and tools. Princeton University Press, Princeton and Oxford
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
Schmidt R. (2003) Tail dependence. Statistical tools in finance and insurance. Springer Verlag, New York, pp 65–91
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
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
Wei W.W.S. (2005) Time series analysis: univariate and multivariate methods. Pearson Addison Wesley, Boston
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
About this article
Cite this article
So, M.K.P., Tse, A.S.L. Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets. Asia-Pac Financ Markets 16, 183–210 (2009). https://doi.org/10.1007/s10690-009-9092-6