This paper investigates whether being a local leader affects a firm's stock price crash risk. We find that local leadership, in terms of being a relatively large firm in a surrounding locality, decreases a firm's stock price crash risk. The results are robust to both an instrumental variable and a difference-in-differences regression approach. We also document that the impact on crash risk depends on the extent to which local communities are likely to be monitoring local firms through their stock market participation rates, the information environment surrounding these firms, and the level of industry competition. Overall, our results highlight a novel benefit of being a local leader, as it is associated with a higher level of local monitoring which renders the firm less prone to crash risk.

In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.