Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement
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Date
2021
Authors
Kithinji, Martin M.
Mwita, Peter N.
Kube, Ananda O.
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Publisher
Abstract
In this paper, we propose an extreme conditional quantile estimator. Derivation of the estimator is based on extreme quantile
autoregression. A noncrossing restriction is added during estimation to avert possible quantile crossing. Consistency of the
estimator is derived, and simulation results to support its validity are also presented. Using Average Root Mean Squared Error
(ARMSE), we compare the performance of our estimator with the performances of two existing extreme conditional quantile
estimators. Backtest results of the one-day-ahead conditional Value at Risk forecasts are also given.
Description
An Article Published in Journal of Probability and Statistics