Tail-GAN：学习模拟尾部风险情景
Tail-GAN: Learning to Simulate Tail Risk Scenarios
AI总结：
本文提出Tail-GAN，利用VaR与ES的联合可引出性训练GAN生成高维多资产价格情景，以准确刻画静态和动态交易策略的尾部风险，并通过PCA提升可扩展性。
摘要：
动态投资组合损失分布的估计需要模拟能够代表其组成部分真实联合动态的情景。我们提出了一种新颖的数据驱动方法，用于模拟真实的高维多资产情景，重点是为一类静态和动态交易策略准确表示尾部风险。我们利用风险价值（VaR）和预期短缺（ES）的联合可引出性，设计了一个生成对抗网络（GAN），该网络学习模拟保留这些尾部风险特征的价格情景。我们通过详细的数值实验，在合成数据集和市场数据集上展示了算法的性能。与此前提出的数据驱动情景生成器相比，我们提出的方法能够正确捕捉广泛交易策略类别的尾部风险，并展现出强大的泛化能力。此外，将我们的方法与输入数据的主成分分析相结合，增强了其对高维多资产时间序列的可扩展性，使我们的框架区别于文献中通常考虑的单变量设置。
英文摘要：
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network (GAN) that learns to simulate price scenarios preserving these tail risk features. We demonstrate the performance of our algorithm on synthetic and market data sets through detailed numerical experiments. In contrast to previously proposed data-driven scenario generators, our proposed method correctly captures tail risk for a broad class of trading strategies and demonstrates strong generalization capabilities. In addition, combining our method with principal component analysis of the input data enhances its scalability to large-dimensional multi-asset time series, setting our framework apart from the univariate settings commonly considered in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
