The literature on conditional event-study methods criticizes standard event-study procedures as being misspecified if events are voluntary and investors are rational. We argue, however, that standard procedures (1) lead to statistically valid inferences, under conditions described in this article; and (2) are often a superior means of inference, even when event-study data are generated exactly as per a class of rational expectations specifications introduced by the conditional methods literature. Our results provide an equilibrium justification for traditional event-study methods, and we suggest how these simple procedures may be combined with conditional methods to improve statistical power in event studies.

Previous research finds the volatility implied by S&P 100 index option prices to be a biased and inefficient forecast of future volatility and to contain little or no incremental information beyond that in past realized volatility. In contrast, we find that implied volatility outperforms past volatility in forecasting future volatility and even subsumes the information content of past volatility in some of our specifications. Our results differ from previous studies because we use longer time series and nonoverlapping data. A regime shift around the October 1987 crash explains why implied volatility is more biased in previous work.

We examine how firms redraw their boundaries after acquisitions using plant-level data. We find that there is extensive restructuring in a short period following mergers and full-firm acquisitions. Acquirers of full firms sell 27% and close 19% of the plants of target firms within three years of the acquisition. Acquirers with skill in running their peripheral divisions tend to retain more acquired plants. Retained plants increase in productivity whereas sold plants do not. These results suggest that acquirers restructure targets in ways that exploit their comparative advantage.