Abstract Threshold selection plays a key role in various aspects of statistical inference rare events. In this work, two new threshold methods are introduced. The first approach measures the fit exponential approximation above and achieves good performance small samples. second method smoothly estimates asymptotic mean squared error Hill estimator performs consistently well over wide range proc...

This article reviews methods from extreme value analysis with applications to risk assessment in finance. It covers three main methodological paradigms: the classical framework for independent and identically distributed data application estimation market operational loss data, multivariate cross-sectional dependent systemic risk, stationary serially applied dynamic management. The is addressed...

In many engineering applications, data samples are expensive to get and limited in number. In such a difficult context, this paper shows how classification based on Reproducing Kernel Hilbert Space (RKHS) can be used in conjunction with Extreme Value Theory (EVT) to estimate extreme multivariate quantiles and small probabilities of failure. For estimating extreme multivariate quantiles, RKHS on...

Abstract: We develop Bayesian nonparametric modeling and inference methods for the analysis of extremes of stochastic processes. We use a point process approach under which the pairwise observations, comprising the time of excesses and the exceedances over a high threshold, are assumed to arise from a non-homogeneous Poisson process. To understand and capture the behavior of rare events, we pro...