Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
The extent of the maximum likelihood estimator for the extreme value index
Journal of Multivariate Analysis
Evaluating Value-at-Risk Models with Desk-Level Data
Management Science
A new class of independence tests for interval forecasts evaluation
Computational Statistics & Data Analysis
Editorial: Risk modelling and management: An overview
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
Threshold methods, based on fitting a stochastic model to the excesses over a threshold, were developed under the acronym POT (peaks over threshold). To eliminate the tendency to clustering of violations, we propose a model-based approach within the POT framework that uses the durations between excesses as covariates. Based on this approach we suggest models for forecasting one-day-ahead Value-at-Risk. A simulation study was performed to validate the estimation procedure. Comparative studies with global stock market indices provide evidence that the proposed models can perform better than state-of-the art risk models and better than the widely used RiskMetrics model in terms of unconditional coverage, clustering of violations and capital requirements under the Basel II Accord.