衡量尾端风险

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics Pub Date : 2024-04-01 DOI:10.1016/j.jeconom.2024.105769

Maik Dierkes , Fabian Hollstein , Marcel Prokopczuk , Christoph Matthias Würsig

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引用次数: 0

摘要

我们全面研究了文献中提出的尾部风险测量方法的实用性。我们评估了它们在统计和经济上的有效性。Bollerslev 和 Todorov（2011b）的期权隐含度量（BT11Q）总体表现最佳。其他一些尾部风险度量方法擅长于专门的任务，而 BT11Q 在所有测试中都表现出色：首先，BT11Q 可以预测未来的尾部事件和未来的尾部波动。其次，它对时间序列和横截面的回报率以及实际经济活动都有预测能力。最后，模拟分析表明，性能的主要驱动因素是测量误差。

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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Measuring tail risk

We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate their statistical as well as their economic validity. The option-implied measure of Bollerslev and Todorov (2011b) ( $B T 11 Q$ ) performs best overall. While some other tail risk measures excel at specialized tasks, $B T 11 Q$ performs well in all tests: First, $B T 11 Q$ can predict both future tail events and future tail volatility. Second, it has predictive power for returns in both the time series and the cross-section, as well as for real economic activity. Finally, a simulation analysis shows that the main driver of performance is measurement error.

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来源期刊

Journal of Econometrics 社会科学-数学跨学科应用

CiteScore

8.60

自引率

1.60%

发文量

220

审稿时长

3-8 weeks

期刊介绍： The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.