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Financial Markets and Portfolio Management

, Volume 32, Issue 2, pp 207–233 | Cite as

The dynamic dependence between stock markets in the greater China economic area: a study based on extreme values and copulas

  • Saiful Izzuan Hussain
  • Steven LiEmail author
Article
  • 152 Downloads

Abstract

This study employs the dynamic copula method and extreme value theory to investigate the dependence structure between pairs of greater China economic area (GCEA) stock markets consisting of Shanghai (SHSE), Shenzhen (SZSE), Hong Kong (HKSE), and Taiwan (TWSE) stock exchanges from July 2000 to June 2017. We also examine the impact of financial crisis on the dependence structure by considering the global financial crisis and the Chinese stock market crash (2015–2016). Many studies have shown that the benefits of portfolio diversification across the stock markets in the same region could be diminishing. However, it is interesting to see that the diversification benefits appear to be viable for investing in some GCEA pairs of stock markets (SHSE–TWSE and SZSE–HKSE).

Keywords

Copula Extreme value theory Dependence structure Chinese stock markets Financial crisis 

JEL Classification

G15 G32 

Notes

Acknowledgments

The authors thank the editor and the referee for their constructive comments and suggestions, which helped to improve the paper significantly.

References

  1. Al Rahahleh N., Bhatti, M.I.: Co-movement measure of information transmission on international equity markets. Phys. A: Stat. Mech. Appl. 470, 119–131 (2017)Google Scholar
  2. Al Rahahleh, N., Bhatti, M.I., Adeinat, I.: Tail dependence and information flow: evidence from international equity markets. Phys. A: Stat. Mech. Appl. 474, 319–329 (2017)Google Scholar
  3. Ané, T., Labidi, C.: Spillover effects and conditional dependence. Int. Rev Econ. Finance 15(4), 417–442 (2006)Google Scholar
  4. Balkema, A.A., De Haan, L.: Residual life time at great age. Ann. Probab. 2(5), 792–804 (1974)Google Scholar
  5. Bartram, S.M., Taylor, S.J., Wang, Y.H.: The Euro and European financial market dependence. J. Bank. Finance 31(5), 1461–1481 (2007)Google Scholar
  6. Bhatti, M.I., Nguyen, C.C.: Diversification evidence from international equity markets using extreme values and stochastic copulas. J. Int. Financ. Mark. Inst. Money 22(3), 622–646 (2012)Google Scholar
  7. Chen, X., Fan, Y.: Estimation of copula-based semiparametric time series models. J. Econom. 130(2), 307–335 (2006)Google Scholar
  8. Chen, J., Jiang, F., Li, H., Xu, W.: Chinese stock market volatility and the role of US economic variables. Pac. Basin Finance J. 39, 70–83 (2016)Google Scholar
  9. Cheng, H., Glascock, J.L.: Dynamic linkages between the greater China economic area stock markets—Mainland China, Hong Kong, and Taiwan. Rev. Quant. Finance Account. 24(4), 343–357 (2005)Google Scholar
  10. Cherubini, U., Luciano, E., Vecchiato, W.: Copula Methods in Finance. Wiley, New York (2004)Google Scholar
  11. Cheung, Y.-W., Chinn, M.D., Fujii, E.: China, Hong Kong, and Taiwan: a quantitative assessment of real and financial integration. China Econ. Rev. 14(3), 281–303 (2003)Google Scholar
  12. Cheung, Y.-W., Chinn, M.D., Fujii, E.: Dimensions of financial integration in greater China: money markets, banks and policy effects. Int. J. Finance Econ. 10(2), 117–132 (2005)Google Scholar
  13. Coles, S., Bawa, J., Trenner, L., Dorazio, P.: An Introduction to Statistical Modeling of Extreme Values, vol. 208. Springer, London (2001)Google Scholar
  14. Danielsson, J., de Vries, C.G.: Value-at-risk and extreme returns. Ann. Econ. Stat. 60, 239–270 (2000)Google Scholar
  15. DuMouchel, W.H.: Estimating the stable index α in order to measure tail thickness: a critique. Ann. Stat. 11(4), 1019–1031 (1983)Google Scholar
  16. Embrechts, P., Resnick, S.I., Samorodnitsky, G.: Extreme value theory as a risk management tool. N. Am. Actuar. J. 3(2), 30–41 (1999)Google Scholar
  17. Embrechts, P., Lindskog, F. and McNeil, A.: Modelling dependence with copulas and applications to risk management. Handbook of Heavy Tailed Distributions in Finance (2001)Google Scholar
  18. Engle, R.F., Ng, V.K.: Measuring and testing the impact of news on volatility. J. Finance 48(5), 1749–1778 (1993)Google Scholar
  19. Fang, L., Bessler, D.A.: Is it China that leads the Asian stock market contagion in 2015? Appl. Econ. Lett. 25(11), 1–6 (2017)Google Scholar
  20. Fermanian, J.D.: Goodness-of-fit tests for copulas. J. Multivar. Anal. 95(1), 119–152 (2005)Google Scholar
  21. Fisher, R.A., Tippett, L.H.C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample. Paper presented to Mathematical Proceedings of the Cambridge Philosophical Society (1928)Google Scholar
  22. Forbes, K.J., Rigobon, R.: No contagion, only interdependence: measuring stock market comovements. J. Finance 57(5), 2223–2261 (2002)Google Scholar
  23. Gençay, R., Selçuk, F., Ulugülyaǧci, A.: High volatility, thick tails and extreme value theory in value-at-risk estimation. Insur. Math. Econ. 33(2), 337–356 (2003)Google Scholar
  24. Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Finance 48(5), 1779–1801 (1993)Google Scholar
  25. Groenewold, N., Tang, S.H.K., Yanrui, W.: The dynamic interrelationships between the greater China share markets. China Econ. Rev. 15(1), 45–62 (2004)Google Scholar
  26. Hans, M.: Estimation and model selection of copulas with an application to exchange rates (No. 056). Maastricht Research School of Economics of Technology and Organization (METEOR) (2007)Google Scholar
  27. He, H., Chen, S., Yao, S., Ou, J.: Stock market interdependence between China and the world: a multi-factor R-squared approach. Finance Res. Lett. 13, 125–129 (2015)Google Scholar
  28. Ho, K.Y., Zhang, Z.: Dynamic linkages among financial markets in the greater China region: a multivariate asymmetric approach. World Econ. 35(4), 500–523 (2012)Google Scholar
  29. Hu, L.: Dependence patterns across financial markets: a mixed copula approach. Appl. Financ. Econ. 16(10), 717–729 (2006)Google Scholar
  30. Hu, J.: Dependence structures in Chinese and US financial markets: a time-varying conditional copula approach. Appl. Financ. Econ. 20(7), 561–583 (2010)Google Scholar
  31. Hussain, S.I., Li, S.: Modeling the distribution of extreme returns in the Chinese stock market. J. Int. Financ. Mark. Inst. Money 34, 263–276 (2015)Google Scholar
  32. Hussain, S.I., Li, S.: The dependence structure between Chinese and other major stock markets using extreme values and copulas. Int. Rev. Econ. Finance (2017).  https://doi.org/10.1016/j.iref.2017.12.002 Google Scholar
  33. Hyde, S., Bredin, D., Nguyen, N.: Chapter 3 Correlation dynamics between Asia-Pacific, EU and US stock returns. In: Asia-Pacific Financial Markets: Integration, Innovation and Challenges, pp. 39–61. Emerald Group Publishing Limited (2007)Google Scholar
  34. Johansson, A.C., Ljungwall, C.: Spillover effects among the greater China stock markets. World Dev. 37(4), 839–851 (2009)Google Scholar
  35. Jondeau, E., Rockinger, M.: The copula-GARCH model of conditional dependencies: an international stock market application. J. Int. Money Finance 25(5), 827–853 (2006)Google Scholar
  36. Li, H.: International linkages of the Chinese stock exchanges: a multivariate GARCH analysis. Appl. Financ. Econ. 17(4), 285–297 (2007)Google Scholar
  37. Longin, F.: The asymptotic distribution of extreme stock market returns. J. Bus. 69(3), 383–408 (1996)Google Scholar
  38. Longin, F., Solnik, B.: Extreme correlation of international equity markets. J. Finance 56(2), 649–676 (2001)Google Scholar
  39. McNeil, A.J.: Calculating quantile risk measures for financial time series using extreme value theory. In: Calculating Quantile Risk Measures for Financial Return Series Using Extreme Value Theory (1998)Google Scholar
  40. McNeil, A.J.: Extreme value theory for risk managers. In: Internal Modelling and CAD II, pp. 93–113. RISK Books (1999)Google Scholar
  41. McNeil, A.J., Frey, R.: Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. J. Empir. Finance 7(3–4), 271–300 (2000)Google Scholar
  42. Meissner, G.: Correlation Risk Modeling and Management: An Applied Guide Including the Basel III Correlation Framework—With Interactive Models in Excel/VBA. Wiley, New York (2013)Google Scholar
  43. Neftci, S.N.: Value at risk calculations, extreme events, and tail estimation. J. Deriv. 7(3), 23–37 (2000)Google Scholar
  44. Nguyen, C., Bhatti, M.I.: Copula model dependency between oil prices and stock markets: evidence from China and Vietnam. J. Int. Financ. Mark. Inst. Money 22(4), 758–773 (2012)Google Scholar
  45. Nguyen, C., Bhatti, M.I., Henry, D.: Are Vietnam and Chinese stock markets out of the US contagion effect in extreme events? Phys. A 480, 10–21 (2017)Google Scholar
  46. Patton, A.J.: Modelling asymmetric exchange rate dependence. Int. Econ. Rev. 47(2), 527–556 (2006a)Google Scholar
  47. Patton, A.J.: Estimation of multivariate models for time series of possibly different lengths. J. Appl. Econom. 21(2), 147–173 (2006b)Google Scholar
  48. Pickands, J.: Statistical inference using extreme order statistics. Ann. Stat. 3(1), 119–131 (1975)Google Scholar
  49. Poon, S.H., Rockinger, M., Tawn, J.: Extreme value dependence in financial markets: diagnostics, models, and financial implications. Rev. Financ. Stud. 17(2), 581–610 (2004)Google Scholar
  50. Rodriguez, J.C.: Measuring financial contagion: a copula approach. J. Empir. Finance 14(3), 401–423 (2007)Google Scholar
  51. Shin, K., Sohn, C.H.: Trade and financial integration in East Asia: effects on co-movements. World Econ. 29(12), 1649–1669 (2006)Google Scholar
  52. Sklar, M.: Fonctions de répartition à n dimensions et leurs marges. Université Paris 8 (1959)Google Scholar
  53. Straetmans, S.: Extreme financial returns and their comovements. Doctoral dissertation, Ph. D. thesis, Tinbergen Institute Research Series (1998)Google Scholar
  54. Wang, K.M.: Did Vietnam stock market avoid the “contagion risk” from China and the US? The contagion effect test with dynamic correlation coefficients. Qual. Quant. 47(4), 2143–2161 (2013)Google Scholar
  55. Wang, Y., Di Iorio, A.: Are the China-related stock markets segmented with both world and regional stock markets? J. Int. Financ. Mark. Inst. Money 17(3), 277–290 (2007)Google Scholar
  56. Wang, K., Chen, Y.-H., Huang, S.-W.: The dynamic dependence between the Chinese market and other international stock markets: a time-varying copula approach. Int. Rev. Econ. Finance 20(4), 654–664 (2011)Google Scholar
  57. Xu, X.E., Fung, H.G.: Information flows across markets: evidence from China-backed stocks dual-listed in Hong Kong and New York. Financ. Rev. 37(4), 563–588 (2002)Google Scholar

Copyright information

© Swiss Society for Financial Market Research 2018

Authors and Affiliations

  1. 1.Graduate School of Business and Law (GSBL)RMIT UniversityMelbourneAustralia
  2. 2.School of Mathematical Sciences, Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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