ABSTRACT This paper studies the relatedness and the model construction of exchange rate volatility and the Thailand’s stock market returns. Empirical results show that we can construct a bivariate IGARCH (1, 1) model with a dynamic conditional correlation (DCC) to analyze the relationship of exchange rate volatility and Thailand’s stock market returns. The average estimation value of the DCC coefficient for these two markets equals to –0.1650, this result indicates that the exchange rate volatility negatively affects the Thailand’s stock market. Empirical result also shows that there do not exist the asymmetrical effect on the Thailand’s exchange rate and Thailand’s stock markets. And the Japan’s stock return volatility truly affects the variation risks of the Thailand stock market. Based on the viewpoint of DCC, the bivariate IGARCH (1, 1) model with a DCC has the better explanation ability compared to the traditional bivariate GARCH (1, 1) model.
Cite this paper
nullW. Horng and C. Chen, "DCC and Analysis of the Exchange Rate and the Stock Market Returns’ Volatility: An Evidence Study of Thailand Country," iBusiness, Vol. 2 No. 3, 2010, pp. 218-222. doi: 10.4236/ib.2010.23027.
 C. Kearney, “The Causes of Volatility in a Small, Internationally Integrated Stock Market: Ireland, July 1975 - June 1994,” Journal of Financial Research, Vol. 21, No. 4, 1998, pp. 85-104.
T. Bollerslev, “Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model,” Review of Economics and Statistics, Vol. 72, No. 3, 1990, pp. 498-505.
C. C. Nieh and C. F. Lee, “Dynamic Relationship between Stock Prices and Exchange Rates for G-7 Countries,” The Quarterly of Economics and Finance, Vol. 41, No. 3, 2001, pp. 477-490.
S. Y. Yang and S. C. Doong, “Price and Volatility Spillovers between Stock Prices and Exchange Rates: Empirical Evidence from the G-7 Countries,” International Journal of Business and Economics, Vol. 3, No. 2, 2004, pp. 139-153.
E. K. Berndt, B. H. Hall, R. E. Hall and J. A. Hausman, “Estimation and Inference in Nonlinear Structural Models,” Annals of Economic and Social Measurement, Vol. 3, No. 4, 1974, pp. 653-665.
D. A. Dickey and W. A. Fuller, “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, Vol. 74, No. 366, 1979, pp. 427-431.
G. Kapetanios, Y. Shin and A. Snell, “Testing for a Unit Root in the Nonlinear STAR Framework,” Journal of Econometrics, Vol. 112, No. 2, 2003, pp. 359-379.
S. Johansen, “Estimation and Hypothesis Testing of Cointegration Vector in Gaussian Vector Autoregressive Models,” Econometrica, Vol. 59, No. 6, 1991, pp. 1551-1580.
H. Akaike, “Information Theory and an Extension of the Maximum Likelihood Principle,” 2nd International Symposium on Information Theory, B. N. Petrov and F. C. Budapest, Eds., Akademiai Kiado, Budapest, 1973, pp. 267-281.
R. F. Engle, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Vol. 50, No. 4, 1982, pp. 987- 1007.
R. S. Tsay, “Analysis of Financial Time Series,” John Wiley & Sons, Inc., New York, 2004.
R. F. Engle and V. K. Ng, “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, Vol. 48, No. 5, 1993, pp. 1749-1777.
R. F. Engle, “Dynamic Conditional Correlation - A Sim-ple Class of Multivariate GARCH Models,” Journal of Business and Economic Statistics, Vol. 20, No. 3, 2002, pp. 339-350.
Y. K. Tse and A. K. C. Tsui, “A Multivariate GARCH Model with Time-Varying Correlations,” Journal of
Business & Economic Statistics, Vol. 20, No. 3, 2002, pp. 351-362.
G. M. Ljung and G. E. P. Box, “On a Measure of Lack of Fit in Time Series Models,” Biometrika, Vol. 65, No. 2, 1978, pp. 297-303.