校正极端条件分位数自回归在风险测量中的应用

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2021-04-07 DOI:10.1155/2021/6697120

Martin M. Kithinji, P. Mwita, Ananda O. Kube

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引用次数: 4

摘要

在本文中，我们提出了一个极端条件分位数估计量。估计量的推导是基于极端分位数自回归。在估计期间添加了非交叉限制，以避免可能的分位数交叉。推导了估计量的一致性，并给出了支持其有效性的仿真结果。使用平均均方根误差（ARMSE），我们将我们的估计器的性能与现有的两个极端条件分位数估计器进行了比较。文中还给出了提前一天条件风险价值预测的回测结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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 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.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.