Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement

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dc.contributor.authorKithinji, Martin M.
dc.contributor.authorMwita, Peter N.
dc.contributor.authorKube, Ananda O.
dc.date.accessioned2021-04-13T06:52:04Z
dc.date.available2021-04-13T06:52:04Z
dc.date.issued2021
dc.descriptionAn Article Published in Journal of Probability and Statisticsen_US
dc.description.abstractIn 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.en_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/21975
dc.language.isoenen_US
dc.publisherHindawi Publisheren_US
dc.titleAdjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurementen_US
dc.typeArticleen_US
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