Value at risk (VaR) and expected shortfall (ES) have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions, external regulations, and risk capital allocation. However, existing VaR estimation approaches fail to accurately reflect downside risks, and the ES estimation technique is quite limited owing to its challenging implementation. This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources. The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation. Specifically, this study proposes a VaR estimator by combining quantile regression with “Mogrifier” recurrent neural networks to capture the “long memory” and “clustering” properties of financial assets; while for estimating ES, this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios. In addition to the typical properties of financial assets, the model design is also consistent with heterogeneous market theory. An empirical application to four major global stock indices shows that our model is superior to other existing models.

中文翻译：

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使用深度分位数回归、基于 GAN 的场景生成和异构市场假设来预测 VaR 和 ES

风险价值（VaR）和预期缺口（ES）已成为检测金融资产市场风险的标准指标，在投资决策、外部监管和风险资本配置中发挥着重要作用。然而，现有的VaR估计方法无法准确反映下行风险，而ES估计技术由于其实施具有挑战性而相当有限。这导致金融机构高估或低估投资风险，最终导致金融资源配置低效。本研究的主要目的是利用机器学习提高VaR估计的准确性，为ES估计提供有效的工具。具体来说，本研究提出了一种 VaR 估计器，将分位数回归与“Mogrifier”循环神经网络相结合，以捕捉金融资产的“长记忆”和“聚类”特性；而为了估计ES，本研究直接对资产分位数进行建模，并利用生成对抗网络来生成未来的尾部风险场景。除了金融资产的典型属性外，模型设计也符合异质市场理论。对全球四大股指的实证应用表明，我们的模型优于其他现有模型。