Disaster and fortune risk in asset returns

Do Disaster risk and Fortune risk fetch a premium or discount in the pricing of individual assets? Disaster risk and Fortune risk are measures for the co-movement of individual stocks with the market, given that the state of the world is extremely bad and extremely good, respectively. To address this question measures of Disaster risk and Fortune risk, derived from statistical Extreme Value Theory, are constructed. The measures are non-parametric and the number of order statistics to be used in the analysis is based on the Kolmogorov-Smirnov distance. This alleviates the problem of an arbitrarily chosen extreme region. The extreme dependence measures are used in Fama-MacBeth cross-sectional asset pricing regressions including Market, Fama-French, Liquidity and Momentum factors. I find that Disaster risk fetches a significant premium of 0.43% for the average stock

Tail index estimation: quantile driven threshold selection

The selection of upper order statistics in tail estimation is notoriously difficult. Most methods are based on asymptotic arguments, like minimizing the asymptotic mse, that do not perform well in finite samples. Here we advance a data driven method that minimizes the maximum distance between the fitted Pareto type tail and the observed quantile. To analyse the finite sample properties of the metric we organize a horse race between the other methods. In most cases the finite sample based methods perform best. To demonstrate the economic relevance of choosing the proper methodology we use daily equity return data from the CRSP database and find economic relevant variation between the tail index estimates