发表机构
Imperial Business School, Imperial College（帝国理工学院商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出机器学习框架，通过在线校准的预测区间量化情景收益不确定性，解决传统压力测试点估计忽视风险因子依赖的问题，并验证了其在真实市场中的有效性。
AI 中文摘要
情景分析被广泛用于金融组合的压力测试，然而传统方法通常使用点估计来概括情景收益，这忽视了受压与未受压风险因素之间的依赖性。我们开发了一个机器学习框架，通过预测区间来量化已实现的下一个交易日情景收益的不确定性，这些区间的宽度可以在线校准。自适应共形情景分析（ACSA）校准特定情景的分位数预测，并为已实现的情景提供长期经验覆盖率保证。我们的主要方法，核情景分析（KSA），直接从指定的压力和当前市场信息中估计情景条件分位数。KSA也可以与ACSA结合，以获得在线校准的预测区间。在旨在反映真实市场的实验中，传统情景分析可能大幅低估风险，包括在标准压力测试下看似安全的组合。我们提出的方法比经验分位数基线实现了更好的校准和更窄的区间。总体而言，该框架通过量化预测不确定性并提供统计验证情景收益的工具，将情景分析从点估计推进到更全面的层面。
英文摘要
Scenario analysis is widely used to stress test financial portfolios, yet conventional approaches often summarize scenario gains using point estimates that overlook dependence between stressed and unstressed risk factors. We develop a machine learning framework for quantifying uncertainty in realized next-day scenario gains through prediction intervals whose widths can be calibrated online. Adaptive conformal scenario analysis (ACSA) calibrates scenario-specific quantile predictions and provides a long-run empirical coverage guarantee over realized scenarios. Our main method, kernel scenario analysis (KSA), estimates scenario-conditional quantiles directly from the specified stress and current market information. KSA can also be combined with ACSA to obtain online-calibrated prediction intervals. In experiments designed to reflect real-world markets, conventional scenario analysis can substantially understate risk, including for portfolios that appear safe under standard stress tests. Our proposed methods achieve better calibration and sharper intervals than empirical-quantile baselines. Overall, the framework moves scenario analysis beyond point estimates by quantifying predictive uncertainty and providing tools for statistically validating scenario gains.
