We employ extreme value theory to identify stock price crashes, featuring low-probability events that produce large, idiosyncratic negative outliers in the conditional distribution. Traditional methods employ approximations under Gaussian assumptions and central moments. This is inherently imprecise and susceptible to misspecifications, especially for tail events. We instead propose new definitions and measures for crash risk based on conditional extremal quantiles (CEQ) of idiosyncratic stock returns. CEQ provide information on quantile-specific impact of covariates, and shed light on prior empirical puzzles and shortcomings in identifying crashes. Additionally, to capture the magnitude of crashes, we provide an expected shortfall analysis of the losses due to crash. Our findings have important implications for a burgeoning literature in financial economics that relies on traditional approximations.