A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence

Analyzing equity market co-movements is important for risk diversification of an international portfolio. Copulas have several advantages compared to the linear correlation measure in modeling co-movement. This paper introduces a copula ARMA-GARCH model for analyzing the co-movement of international equity markets. The model is implemented with an ARMA-GARCH model for the marginal distributions and a copula for the joint distribution. After goodness of fit testing, we find that the Student’s t copula ARMA(1,1)-GARCH(1,1) model with fractional Gaussian noise is superior to alternative models investigated in our study where we model the simultaneous co-movement of nine international equity market indexes. This model is also suitable for capturing the long-range dependence and tail dependence observed in international equity markets. Rachev’s research was supported by grants from Division of Mathematical, Life and Physical Science, College of Letters and Science, University of California, Santa Barbara, and the Deutschen Forschungsgemeinschaft (DFG). Sun’s research was supported by grants from the Deutschen Forschungsgemeinschaft (DFG) and Chinese Government Award for Outstanding Ph.D Students Abroad 2006, No. 2006-180. Kalev’s research was supported with a NCG grant from the Faculty of Business and Economics, Monash University. Data are supplied by Securities Industry Research Center of Asia-Pacific (SIRCA) on behalf of Reuters. The constructive comments of two anonymous referees, the Associate Editor, A.S. Wirjanto, and the Editor-in-charge, Baldev Raj, are gratefully acknowledged. The reviewers and editors are not responsible for any residual errors and omissions.