To understand the dynamics of futures trading and stock market volatility, this study examines the relationships among the salient endogenous variables: stock market volatility, futures return, trading volume, and open interest. We use a structural vector auto-regression (SVAR) model, which is identified based on the empirical results of previous studies and reasonable inferences about capital markets. To just-identify the SVAR model, first, we assume that futures return shock does not have a contemporaneous effect on stock market volatility. This does not mean that it has no effect at all on stock market volatility. In fact, we conjecture that it must have some effect on the stock market volatility through lagged relationships among the variables. Second, futures return and trading volume shocks are assumed to have temporary effects on each variable. For these restrictions, we follow the method of Blanchard and Quah (1989). Finally, we believe open interest shock does not have contemporaneous effects on stock market volatility, futures return and trading volume because open interest shock is generally viewed as a natural aftermath of trading. Given the above just-identification of our SVAR model, we investigate the contemporaneous, lagged, and overall effects by over-identifying restrictions. The overall effect is classified into two components: contemporaneous and lagged effects. As known, a contemporaneous effect is defined as a concurrent impact of each shock on each variable. Lagged effects are estimated from the reduced-form VAR. Observing these two types of effects, we study the nature of the dynamic relations that exist among endogenous variables. A graphical representation of dynamic relations could be seen from impulse-response function analysis. Variance decomposition is also done to evaluate relative importance of each shock. Our major empirical findings are as follows. First, the volatility shock of stock market increases futures trading volume concurrently. Futures traders are very sensitive to spot volatility shock. In the sense of Black (1986) and Hong (2000), informed and uninformed traders quickly adjust their futures positions based on asymmetric information; consequently, such behavior increases futures trading volume. Second, the responses of open interest to other shocks reflect demand characteristics of hedgers who are not informed. If open interest is determined by hedgers' demand as mentioned by Bessembinder and Seguin (1993), the contemporaneous effects of various shocks on open interest show the demand characteristics of hedgers, i.e., uninformed traders. The positive effect of volatility shock on open interest implies that volatility shock increases hedgers' open interest. This is interpreted as hedgers' behavior to manage spot market volatility. Statistically insignificant is the effect of futures return shock on open interest, which is concurrent irrelevance of futures return with hedgers' demand behavior. On the other hand, volume shock decreases open interest concurrently. We interpret this phenomenon as hedgers' liquidation of open interest, which results in increases in the trading volume. Third, futures trading plays a positive role in decreasing stock market volatility. The negative effect of volume shock on stock market volatility is consistent with the fact that the stock market uncertainty may be decreased by noise traders' behavior. This finding supports the results of Cox (1976), Danthine (1978), Kwon and Park (1997), and Ohk (2005). Fourth, the impulse response function analysis also supports the results of hypothesis tests. Responses of volatility to various shocks fade away much more slowly than those of other variables. Stock market volatility does not respond to open interest at all. Negative response of stock market volatility to volume shock disappears more than twenty days later. On the other hand, responses of futures return, trading volume, and open interest to various shocks quickly disappear within 2 days at most. This behavior is consistent with the results of reducedand structural-form VAR estimation. Fifth, variance decomposition results show relative importance of each shock. Stock market volatility is explained by its own shock (69.48%) and volume shock (24.08%), while most of both futures return and open interest are explained by their own shock (94.02%) in the long-run. Similar to the case of stock market volatility, trading volume is also explained by volatility shock (66.56%) and its own shock (32.09%). These results confirm the close relationship between stock market volatility and trading volume. Finally, test results of hypotheses re-confirm the importance of concurrent relationships and indirect lag effects among overall effects. The validity of reduce-form VAR, however, is not evinced by the fact that indirect lag effects are important, because the contemporaneous effects are too substantial to overlook. Hence, we conclude that the SVAR estimation, accompanied by impulse response function and variance decomposition analyses, is an appropriate method for studying the relationships among the stock and futures market variables. While many other studies have previously investigated the same issue, our study stands out from the rest in research methodology. Most of previous studies estimate the dynamic relationships among the same variables as in this paper, but they used reduced-form VAR. This means that they are not able to consider contemporaneous effects of each variable. Unlike them, this study investigates an important issue of the dynamic relationships among stock and futures market variables using structural VAR. In this way, this paper makes an important contribution to extant research by suggesting that contemporaneous effects should be considered.