用对抗生成学习条件风险场景，提升金融风险预测稳定性。
Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios
- 生成器与下游风险目标对齐，通过对抗训练优化条件风险模拟。
- 在标普500数据上，生成场景比基准方法更贴近真实尾部风险。
- 适合金融风控、资产配置等需要稳健风险评估的场景。
我们提出生成对抗回归（GAR），一种通过生成器与下游风险目标对齐来学习条件风险场景的框架。GAR基于可表征的条件风险定义，涵盖分位数、期望值及联合可表征对（如VaR、ES）。将点预测扩展至生成建模，训练生成器使其政策诱导的风险与真实数据在相同上下文下的风险一致。为确保跨所有策略的鲁棒性，GAR采用极小极大形式，由对抗策略识别风险评估中的最坏差异，生成器则相应调整以消除这些差异。该结构保持了与风险函数在广泛策略类中的对齐性，而非固定预设集合。通过基于联合可表征的（VaR, ES）尾部风险实例化，实验在标普500数据上显示，GAR生成的场景在下游风险评估中表现更优，且在对抗策略下仍保持稳定。
原文摘要 · Abstract (English)
We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a regression characterization of conditional risk for elicitable functionals, including quantiles, expectiles, and jointly elicitable pairs. We extend this principle from point prediction to generative modeling by training generators whose policy-induced risk matches that of real data under the same context. To ensure robustness across all policies, GAR adopts a minimax formulation in which an adversarial policy identifies worst-case discrepancies in risk evaluation while the generator adapts to eliminate them. This structure preserves alignment with the risk functional across a broad class of policies rather than a fixed, pre-specified set. We illustrate GAR through a tail-risk instantiation based on jointly elicitable $(\mathrm{VaR}, \mathrm{ES})$ objectives. Experiments on S\&P 500 data show that GAR produces scenarios that better preserve downstream risk than unconditional, econometric, and direct predictive baselines while remaining stable under adversarially selected policies.
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