Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8919477 | Econometrics and Statistics | 2018 | 20 Pages |
Abstract
A general way to study the extremes of a random variable is to consider the family of its Wang distortion risk measures. This class of risk measures encompasses several indicators such as the classical quantile/Value-at-Risk, the Tail-Value-at-Risk and Conditional Tail Moments. Trimmed and winsorised versions of the empirical counterparts of extreme analogues of Wang distortion risk measures are considered. Their asymptotic properties are analysed, and it is shown that it is possible to construct corrected versions of trimmed or winsorised estimators of extreme Wang distortion risk measures who appear to perform overall better than their standard empirical counterparts in difficult finite-sample situations when the underlying distribution has a very heavy right tail. This technique is showcased on a set of real fire insurance data.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Jonathan El Methni, Gilles Stupfler,