ABSTRACT We investigated the relationship between return volatility and trading volume as a proxy for the arrival of information to the market, based on Korean stock market (KSM) data from January 2000 to December 2010. We measured the rela- tionship between return volatility and trading volume using the GJR-GARCH and exponential GARCH (EGARCH) models. We found a positive relationship between trading volume and volatility, suggesting that trading volume influ- ences the flow of information to the market. This finding supports the validity of the mixture of distributions hy-pothesis. Considering that trading volume can also explain volatility asymmetry, we conclude that trading volume is a useful tool for predicting the volatility dynamics of the KSM.
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K. Choi, Z. Jiang, S. Kang and S. Yoon, "Relationship between Trading Volume and Asymmetric Volatility in the Korean Stock Market," Modern Economy, Vol. 3 No. 5, 2012, pp. 584-589. doi: 10.4236/me.2012.35077.
 D. B. Nelson, “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol. 59, No. 2, 1991, pp. 323-370. doi:10.2307/2938260
 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. 1749-1778.
 T. E. Copeland, “A model of Asset Trading under the Assumption of Sequential Information Arrival,” Journal of Finance, Vol. 31, No. 4, 1976, pp. 1149-1168.
 P. K. Clark, “A Subordinated Sto-chastic Process Model with Finite Variance for Speculative Prices,” Economet- rica, Vol. 41, No. 1, 1973, pp. 135-155.
 T. W. Epps and M. L. Epps, “The stochastic Dependence of Security Price Changes and Transac-tion Volumes: Im- plications for the Mixture-of-Distributions Hypothesis,” Econometrica, Vol. 44, No. 2, 1976, pp. 305-321.
 L. Harris, “Cross-Security Tests of the Mixture of Distri- butions Hypothesis,” Journal Financial and Quantitative Analysis, Vol. 21, No. 1, 1986, pp. 39-46.
 C. G. Lamoureux and W. D. La-strapes, “Heteroskedastic- ity in Stock Return Data: Volume versus GARCH Ef- fects,” Journal of Finance, Vol. 45, No. 1, 1990, pp. 221- 229. doi:10.1111/j.1540-6261.1990.tb05088.x
 G. M. Gallo and B. Pacini, “The Effects of Trading Ac- tivity on Market Vola-tility,” European Journal of Fi- nance, Vol. 6, No. 2, 2000, pp. 163-175.
 A. J. Foster, “Vo-lume-Volatility Relationship for Crude Oil Futures Markets,” Journal of Futures Markets, Vol. 15, No. 8, 1995, pp. 929-951.
 A. Alsubaie and M. Najand, “Trading Volume, Time- Varying Conditional Volatility, and Asymmetric Volatil- ity Spillover in the Saudi Stock Market,” Journal of Mul- tinational Financial Management, Vol. 19, No. 2, 2009, pp. 139-159. doi:10.1016/j.mulfin.2008.09.002
 J. L. Sharma, M. Mbodja and R. Kamath, “Heteroscedas- ticity in Stock Market Indicator Return Data: Volume versus GARCH Effects,” Applied Financial Economics, Vol. 6, No. 4, 1996, pp. 337-342.
 C. S. Lee, “A Study on the Trading Volume and Market Volatility,” Journal of Industrial Economics and Business, Vol. 22, No. 2, 2009, pp. 495-511.
 S. C. An, S. W. Jang and S. H. Lee, “A Study on the Lead-Lag Relation between the Trading Volume and the Return Volatility in the KSE,” Management & Economy Re-search, Vol. 14, No. 1, 2006, pp. 19-33.
 S. A. Kim and Y. J. Kim, “An Examination of the Return Volatility-Volume Re-lationship using TGARCH Model in KOSPI200 Futures,” Journal of Industrial Economics and Business, Vol. 21, No. 3, 2008, pp. 1161-1181.
 R. F. Engle, “Autoregressvie condi-tional heteroscedastic- ity with Estimates of the variance of United Kingdom in- flation,” Ecomometrica, Vol. 50, No. 4, 1982, pp. 987- 1007. doi:10.2307/1912773
 T. Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometric, Vol. 31, No. 3, 1986, pp. 307-327. doi:10.1016/0304-4076(86)90063-1
 L. R. Glosten, R. Jagannathan and D. E. Runkle, “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Fi- nance, Vol. 48, No. 5, 1993, pp. 1779-1801.