Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement. (8th April 2021)

• Record Type: Journal Article
• Authors: Kithinji, Martin M.; Mwita, Peter N.; Kube, Ananda O.
• Other Names: Thavaneswaran Aera Academic Editor.
• Is Part Of: Journal of probability and statistics. Volume 2021(2021)
• Publication Date: 2021-04-08
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2021/6697120
• Languages: English
• ISSNs: 1687-952X

Abstract

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.