A Study on the Relationship between Volatility and Trading Volumes Using a Surprising-Information-Stochastic-Volatility(SISV) Model (in Korean)

Modifying the MDH(mixture of distribution hypothesis) theory, Park(2007) showed that the effect of ‘surprising information' on the relationship between volatility and trading volumes contrasts with that of general information. On the basis of his study, this paper proposes surprising-information-stochastic-volatility(SISV) model to capture their nonlinear relationship that is caused by the state change of volatility due to the surprising information flow. To estimate the SISV model efficiently this paper also suggests Markov chain Monte Carlo(MCMC) method. Strong evidence in favor of SISV model over the standard stochastic volatility model is based on empirical application with high frequency data of Won/Dollar exchange rates. Interestingly, while their positive relationship is not significant in the stochastic volatility model with a volume variable, it becomes significant and the persistence of volatility is remarkably reduced in the SISV model. According to the estimation results of the bivariate SISV model, furthermore, the surprising information flow increases the volatility of returns highly, whereas it little changes the volatility of trading volume. These empirical findings are consistent with the modified MDH and imply that ignoring the feature of surprising information can lead to a model misspecification.