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New Evidence of Asymmetric Dependence Structures in International Equity Markets

Published online by Cambridge University Press:  06 April 2009

Tatsuyoshi Okimoto
Tatsuyoshi Okimoto
Affiliation:
[email protected], Graduate School of International Corporate Strategy, Hitotsubashi University, National Center of Sciences, 2–1–2 Hitotsubashi, Chiyoda-ku, Tokyo101–8439, Japan.
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Abstract

A number of recent studies finds two asymmetries in dependence structures in international equity markets; specifically, dependence tends to be high in both highly volatile markets and in bear markets. In this paper, a further investigation of asymmetric dependence structures in international equity markets is performed by using the Markov switching model and copula theory. Combining these two theories enables me to model dependence structures with sufficient flexibility. Using this flexible framework, I indeed find that there are two distinct regimes in the U. S.-U. K. market. I also show that for the U. S.-U. K. market the bear regime is better described by an asymmetric copula with lower tail dependence with clear rejection of the Markov switching multivariate normal model. In addition, I show that ignorance of this further asymmetry in bear markets is very costly for risk management. Lastly, I conduct a similar analysis for other G7 countries, where I find other cases in which the use of a Markov switching multivariate normal model would be inappropriate.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 43 , Issue 3 , September 2008 , pp. 787 - 815
Copyright
Copyright © School of Business Administration, University of Washington 2008

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References

Ang, A., and Bekaert, G.. “International Asset Allocation with Regime Shifts.” Review of Financial Studies, 15 (2002), 1137–1187.CrossRefGoogle Scholar
Ang, A., and Chen, J.. “Asymmetric Correlations of Equity Portfolios.” Journal of Financial Economics, 63 (2002), 443–494.CrossRefGoogle Scholar
Ball, C. A., and Torous, W. N.. “Stochastic Correlation across International Stock Markets.” Journal of Empirical Finance, 7 (2000), 373–388.CrossRefGoogle Scholar
Bekaert, G., and Wu, G.. “Asymmetric Volatility and Risk in Equity Markets.” Review of Financial Studies, 13 (2000), 1–42.CrossRefGoogle Scholar
Breymann, W.; Dias, A.; and Embrechts, P.. “Dependence Structures for Multivariate High-Frequency Data in Finance.” Quantitative Finance, 3 (2003), 1–16.CrossRefGoogle Scholar
Campbell, R.; Koedijk, K.; and Kofman, P.. “Increased Correlation in Bear Markets.” Financial Analysts Journal, 58 (2002), 87–94.CrossRefGoogle Scholar
Carrasco, S.; Hu, L.; and Ploberger, W.. “Optimal Test for Markov Switching.” Working Paper, University of Rochester (2004).Google Scholar
Das, S., and Uppal, R.. “International Portfolio Choice with Systemic Risk.” Journal of Finance, 59 (2004), 2809–2834.CrossRefGoogle Scholar
Davison, A. C., and Smith, R. L.. “Models for Exceedances over High Thresholds.” Journal of the Royal Statistical Society, 52 (1990), 393–442.Google Scholar
Dempster, A. P.; Laird, N. M.; and Rubin, D. B.. “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society B, 39 (1977), 1–38.Google Scholar
Diebold, F. X.; Lee, J.-H.; and Weinbach, G. C.. “Regime Switching with Time-Varying Transition Probabilities.” In Nonstationary Time-Series Analysis and Cointegration, Hargreaves, C., eds. Oxford, U. K: Oxford University Press (1994), 283–302.CrossRefGoogle Scholar
Durland, J. M., and McCurdy, T. H.. “Duration-Dependent Transitions in a Markov Model of U. S. GNP Growth.” Journal of Business and Economic Statistics, 12 (1994), 279–288.CrossRefGoogle Scholar
Embrechts, P.; Lindskog, F.; and McNeil, A.. “Modelling Dependence with Copulas and Applications to Risk Management.” In Handbook of Heavy Tailed Distributions in Finance, Rachev, S., eds. Amsterdam: Elsevier (2003), 329–384.CrossRefGoogle Scholar
Embrechts, P.; McNeil, A.; and Straumann, D.. “Correlation and Dependence Properties in Risk Management: Properties and Pitfalls.” In Risk Management: Value at Risk and Beyond, Dempster, M., eds. Cambridge, U. K: Cambridge University Press (2002), 176–223.CrossRefGoogle Scholar
Engle, R. F. “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business and Economic Statistics, 20 (2002), 339–350.CrossRefGoogle Scholar
Erb, C. B.; Harvey, C. R.; and Viskanta, T. E.. “Forecasting International Correlation.” Financial Analysts Journal, 50 (1994), 32–45.CrossRefGoogle Scholar
Fang, K.-T.; Kotz, S.; and Ng, K. W.. Symmetric Multivariate and Related Distributions. London, U. K: Chapman & Hall (1987).Google Scholar
Filardo, A. J. “Business-Cycle Phases and Their Transitional Dynamics.” Journal of Business and Economic Statistics, 12 (1994), 299–308.CrossRefGoogle Scholar
Guidolin, M., and Timmermann, A.. “Term Structure of Risk under Alternative Econometric Specifications.” Journal of Econometrics, 131 (2006), 285–308.CrossRefGoogle Scholar
Hamao, Y.; Masulis, R. W.; and Ng, V.. “Correlations in Price Changes and Volatility across International Stock Markets.” Review of Financial Studies, 3 (1990), 281–308.CrossRefGoogle Scholar
Hamilton, J. D. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57 (1989), 357–384.CrossRefGoogle Scholar
Hamilton, J. D. “Analysis of Time Series Subject to Changes in Regime.” Journal of Econometrics, 45 (1990), 39–70.CrossRefGoogle Scholar
Joe, H.Multivariate Models and Dependence Concepts. London, U. K: Chapman & Hall (1997).Google Scholar
Jondeaua, E., and Rockinger, M.. “The Copula-GARCH Model of Conditional Dependencies: An International Stock Market Application.” Journal of International Money and Finance, 25 (2006), 827–853.CrossRefGoogle Scholar
King, M.; Sentana, E.; and Wadhwani, S.. “Volatility and Links between National Stock Markets.” Econometrica, 62 (1994), 901–933.CrossRefGoogle Scholar
King, M., and Wadhwani, S.. “Transmission of Volatility between Stock Markets.” Review of Financial Studies, 3 (1990), 5–33.CrossRefGoogle Scholar
Ledford, A. W., and Tawn, J. A.. “Statistics for Near Independence in Multivariate Extreme Values.” Biometrika, 55 (1997), 169–187.Google Scholar
Lin, W. L.; Engle, R. F.; and Ito, T.. “Do Bulls and Bears Move across Borders? International Transmission of Stock Returns and Volatility.” Review of Financial Studies, 7 (1994), 507–538.CrossRefGoogle Scholar
Longin, F., and Solnik, B.. “Is the Correlation in International Equity Returns Constant: 1960–1990?” Journal of International Money and Finance, 14 (1995), 3–26.CrossRefGoogle Scholar
Longin, F., and Solnik, B.. “Extreme Correlation of International Equity Markets.” Journal of Finance, 56 (2001), 649–676.CrossRefGoogle Scholar
Mashal, R., and Zeevi, A.. “Beyond Correlation: Extreme Co-Movements between Financial Assets.” Working Paper, Columbia University (2002).Google Scholar
Nelsen, R. B.An Introduction to Copulas (Lecture Notes in Statistics). New York, NY: Springer-Verlag (1999).CrossRefGoogle Scholar
Patton, A. “On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation.” Journal of Financial Econometrics, 2 (2004), 130–168.CrossRefGoogle Scholar
Patton, A. “Modelling Asymmetric Exchange Rate Dependence.” International Economic Review, 47 (2006), 527–556.CrossRefGoogle Scholar
Perez-Quiros, G., and Timmermann, A.. “Business Cycle Asymmetries in Stock Returns: Evidence from Higher Order Moments and Conditional Densities.” Journal of Econometrics, 103 (2001), 259–306.CrossRefGoogle Scholar
Pericoli, M., and Sbracia, M.. “A Primer on Financial Contagion.” Journal of Economic Surveys, 17 (2003), 571–608.CrossRefGoogle Scholar
Poon, S. H.; Rockinger, M.; and Tawn, J.. “Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications.” Review of Financial Studies, 17 (2006), 581–610.CrossRefGoogle Scholar
Ramchand, L., and Susmel, R.. “Volatility and Cross Correlation across Major Stock Markets.” Journal of Empirical Finance, 5 (1998), 397–416.CrossRefGoogle Scholar
Rodriguez, J. C. “Measuring Financial Contagion: A Copula Approach.” Journal of Empirical Finance, 14 (2007), 401–423.CrossRefGoogle Scholar
Scarsini, M. “On Measures of Concordance.” Stochastica, 8 (1984), 201–218.Google Scholar
Sklar, A. “Fonctions de Répartition á n Dimensions et Leurs Marges.” Publications de l’ Institut Statistique de l'Université de Paris, 8 (1959), 229–231.Google Scholar
Timmermann, A. “Moments of Markov Switching Models.” Journal of Econometrics, 96 (2000), 75–111.CrossRefGoogle Scholar
Tse, Y. K., and Tsui, A. K. C.. “A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations.” Journal of Business and Economic Statistics, 20 (2002), 351–362.CrossRefGoogle Scholar