Dependence Structure of Korean Financial Markets Using Copula-GARCH Model Kim, Woohwan;
This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJR-GARCH with skewed elliptical distributions and four copulas (Gaussian, Student's t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from May 2006 to June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.
Alouia, R., Aissa, M. S. B. and Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach, Journal of International Money and Finance, 32, 719-738.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 300-328.
Dobric, J. and Schmid, F. (2005). Nonparametric estimation of the lower tail dependence in bivariate copulas, Journal of Applied Statistics, 32, 387-407.
Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models, Journal of Business and Economic Statistics, 20, 339-350.
Glosten, L. R., Jagannathan, R. and Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779-1801.
Hillebrand, E. (2005). Neglecting parameter changes in GARCH models, Journal of Econometrics, 129, 121-138.
Huang, J. J., Lee, K. J., Liang, H. and Lina, W. F. (2009). Estimating value at risk of portfolio by conditional copula-GARCH method, Insurance: Mathematics and Economics, 45, 315-324
Joe, H. (1997). Multivariate models and dependence concepts, Monographs in Statistics and Probability 73, Chapman and Hall, London.
Jondeau, E. and Rockinge, M. (2006). The Copula-GARCH model of conditional dependencies: An international stock market application, Journal of International Money and Finance, 25, 827-853.
Liu, Y. and Luger, R. (2009). Efficient estimation of copula-GARCH models, Computational Statistics & Data Analysis, 53, 2284-2297.
Marshal, R. and Zeevi, A. (2002). Beyond correlation: Extreme comovements between financial assets, Working Paper, Columbia Business.
Mikosch, T. and Starica, C. (2004). Nonstationarities in financial times series, the long range dependence and the IGARCH effects, The Review of Economics and Statistics, 86, 378-390.
Nelsen, R. B. (2006). An Introduction to Copulas, Springer, Second edition, New York.
Pesaran, B. and Pesaran, M. H. (2007). Modelling volatilities and conditional correlations in futures markets with a multivariate t distribution, Cambridge Working Papers in Economics 0734.