用于模拟多资产尾部风险情景的尾部GAN

文章 arXiv papers · 作者: Rama Cont et al.

总结

尾部GAN是一种数据驱动的情景生成器，旨在模拟高维多资产投资组合中逼真的联合价格动态，重点关注分布尾部的损失。它利用风险价值和预期损失的联合可陈述性训练生成对抗网络，以保留这些风险特征。其预期用途包括在模拟市场情景下评估静态和动态交易策略。

论文报告了基于合成数据和市场数据的数值实验。据称，该方法能够捕捉多类策略的尾部风险，并推广到训练样本之外。研究还报告称，对输入数据应用主成分分析可以提升大维度时间序列的可扩展性。所提供的描述没有具体说明资产、评估指标或对比结果，因此无法根据该摘要独立评估这些说法。情景质量仍取决于输入数据的代表性，也无法确保未来的尾部事件与模拟结果相符。

核心观点

尾部GAN生成多资产价格动态的联合情景，重点关注投资组合尾部损失。
模型将风险价值和预期损失的联合可陈述性作为训练目标。
该框架旨在评估静态和动态交易策略。
基于合成数据和市场数据的实验报告称，该方法能够捕捉尾部风险并推广到不同策略。
研究使用主成分分析提升大维度时间序列的可扩展性。

# Tail-GAN: Learning to Simulate Tail Risk Scenarios


# Tail-GAN: Learning to Simulate Tail Risk Scenarios









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.

在遵守原作品许可的前提下，附作者信息全文展示。许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。

用于模拟多资产尾部风险情景的尾部GAN

总结

核心观点

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