基于混合提示-检索管道的LLM生成反事实压力情景用于投资组合风险模拟
LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline
AI总结：
本文提出了一种混合提示-检索管道，利用LLM生成宏观经济压力情景，通过结构化提示和检索机制，实现投资组合风险模拟的可解释和可验证方法。
摘要：
我们开发了一个透明且完全可审计的基于大语言模型的管道，用于宏观经济金融压力测试，结合了结构化提示和可选的国家基本面和新闻检索。该系统为G7生成可机器阅读的宏观经济情景，涵盖GDP增长、通胀和利率，并通过基于因子的映射转换为投资组合损失，从而通过价值-at-风险和预期短缺评估相对于经典计量经济学基线。在不同模型、国家和检索设置下，LLM生成的连贯且国家特定的压力叙述产生稳定的尾部风险放大，对检索选择的敏感性有限。全面的合理性检查、情景诊断和基于方差分解的ANOVA显示，风险变化主要由投资组合构成和提示设计驱动，而非检索机制。该管道包含快照、确定性模式和哈希验证的艺术品，以确保可重复性和可审计性。总体而言，结果表明，当与透明结构和严格验证结合时，LLM生成的宏观经济情景可以为传统压力测试框架提供可扩展和可解释的补充。
英文摘要：
We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses through a factor-based mapping that enables Value-at-Risk and Expected Shortfall assessment relative to classical econometric baselines. Across models, countries, and retrieval settings, the LLMs produce coherent and country-specific stress narratives, yielding stable tail-risk amplification with limited sensitivity to retrieval choices. Comprehensive plausibility checks, scenario diagnostics, and ANOVA-based variance decomposition show that risk variation is driven primarily by portfolio composition and prompt design rather than by the retrieval mechanism. The pipeline incorporates snapshotting, deterministic modes, and hash-verified artifacts to ensure reproducibility and auditability. Overall, the results demonstrate that LLM-generated macro scenarios, when paired with transparent structure and rigorous validation, can provide a scalable and interpretable complement to traditional stress-testing frameworks.
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。
