Forecasting Value-at-Risk with a duration-based POT method
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. (C) 2012 Published by Elsevier B.V. on behalf of IMACS.
quantitative risk management statistics of extremes financial timeseries