Discussion paper
DP18524 Forecasting crashes with a smile
We derive option-implied bounds on the probability of a crash in an individual stock, and argue that the lower bound should be close to the truth a priori. Empirically, the lower bound successfully forecasts crashes both in and out of sample; and it outperforms models based on stock characteristics previously studied in the literature. In a multivariate regression, a one standard deviation increase in the bound raises the predicted crash probability by 3 percentage points, whereas a one standard deviation increase in the next most important predictor (a measure of short interest) raises the predicted probability by only 0.3 percentage points.
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