This paper studies stock market volatility in Korea using a semiparametricsingle index volatility model, in which a single index long run componentinduced by exogenous covariates is multiplied to a GARCH short runcomponent. When a covariate is nonstationary, i.e. integrated or nearintegrated,the model can account for time-varying unconditional variance offinancial time series. Among various economic and nancial indicators, it isfound that the coincident composite index, VKOSPI, and housing price indexare helpful in fitting and forecasting stock market volatility in Korea. It isshown that the model using these three variables outperforms standard modelsboth in terms of in-sample fitting and out-of-sample forecasting.