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首页> 外文期刊>Journal of Economic Dynamics and Control >Dynamic expected shortfall: A spectral decomposition of tail risk across time horizons
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Dynamic expected shortfall: A spectral decomposition of tail risk across time horizons

机译：动态预期不足：跨时间跨度的尾部风险频谱分解

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摘要

The tail risk of financial institutions is traditionally measured by Expected Shortfall (ES) that does not characterize risk changes over investment horizons. Using wavelet analysis, we propose a new method to capture the dynamics of ES across time horizons. The new method decomposes the stock return of financial institutions into different frequency (e.g., short-, mid-, and long-run) components, and then, models the dynamics of these components separately to produce an aggregated ES forecast. We provide numerical and empirical examples to illustrate the new method. We also study the relevance of each frequency component to out-of-sample ES forecasts over different predictive horizons. Our empirical results confirm that the different frequency components of stock returns exhibit different persistence. Explicitly considering this distinction when modeling ES significantly improves the out-of-sample forecasting performance. In addition, excluding the long-run (e.g., yearly) return component can largely reduce short-run (e.g., weekly or monthly) ES forecasts without impacting the regulatory quality of the risk assessment. (C) 2019 Elsevier B.V. All rights reserved.
机译：传统上，金融机构的尾部风险是通过预期缺口（ES）来衡量的，该预期缺口没有体现投资期限内风险变化的特征。使用小波分析，我们提出了一种捕获跨时间跨度ES动态的新方法。新方法将金融机构的股票收益分解为不同的频率（例如短期，中期和长期）成分，然后分别对这些成分的动力学建模以产生汇总的ES预测。我们提供了数值和经验示例来说明该新方法。我们还研究了每个频率分量与不同预测范围内的样本外ES预测的相关性。我们的经验结果证实，股票收益率的不同频率成分表现出不同的持续性。在对ES建模时，明确考虑此区别可显着提高样本外预测性能。此外，排除长期（例如，年度）回报部分可以大大减少短期（例如，每周或每月）的ES预测，而不会影响风险评估的监管质量。 （C）2019 Elsevier B.V.保留所有权利。

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