Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement
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 s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2021/6697120 |
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| _version_ | 1832550254238498816 |
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| author | Martin M. Kithinji Peter N. Mwita Ananda O. Kube |
| author_facet | Martin M. Kithinji Peter N. Mwita Ananda O. Kube |
| author_sort | Martin M. Kithinji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1290cf7b266e437babc5b38ef1e1e673 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-1290cf7b266e437babc5b38ef1e1e6732025-02-03T06:07:17ZengWileyJournal of Probability and Statistics1687-952X1687-95382021-01-01202110.1155/2021/66971206697120Adjusted Extreme Conditional Quantile Autoregression with Application to Risk MeasurementMartin M. Kithinji0Peter N. Mwita1Ananda O. Kube2Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation, P.O. Box 62000, Nairobi 00200, KenyaDepartment of Mathematics, Machakos University, P.O. Box 136, Machakos 90100, KenyaDepartment of Statistics and Actuarial Sciences, Kenyatta University, P.O. Box 43844, Nairobi 00100, KenyaIn 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.http://dx.doi.org/10.1155/2021/6697120 |
| spellingShingle | Martin M. Kithinji Peter N. Mwita Ananda O. Kube Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement Journal of Probability and Statistics |
| title | Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement |
| title_full | Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement |
| title_fullStr | Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement |
| title_full_unstemmed | Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement |
| title_short | Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement |
| title_sort | adjusted extreme conditional quantile autoregression with application to risk measurement |
| url | http://dx.doi.org/10.1155/2021/6697120 |
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