Robust Estimators in Generalized Pareto Models

Details Robust Estimators in Generalized Pareto Models Robust Estimators in Generalized Pareto Models

2010-05-10

arxiv.org/abs/1005.1476v6

Economy

Quantitative Finance

Statistical Finance

26pages, 6 figures

Scientific paper

10.1080/02331888.2011.628022

This paper deals with optimally-robust parameter estimation in generalized Pareto distributions (GPDs). These arise naturally in many situations where one is interested in the behavior of extreme events as motivated by the Pickands-Balkema-de Haan extreme value theorem (PBHT). The application we have in mind is calculation of the regulatory capital required by Basel II for a bank to cover operational risk. In this context the tail behavior of the underlying distribution is crucial. This is where extreme value theory enters, suggesting to estimate these high quantiles parameterically using, e.g. GPDs. Robust statistics in this context offers procedures bounding the influence of single observations, so provides reliable inference in the presence of moderate deviations from the distributional model assumptions, respectively from the mechanisms underlying the PBHT.

Affiliated with

Also associated with

No associations

Rating

Robust Estimators in Generalized Pareto Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust Estimators in Generalized Pareto Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Estimators in Generalized Pareto Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-627309