用大模型生成宏观经济压力情景，辅助投资组合风险评估。
LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline
- 结合提示工程与检索增强生成，构造可读的经济情景。
- 生成的场景能稳定反映尾部风险，对检索依赖低。
- 适合金融风控、量化研究者用于可解释的压力测试。
我们构建了一个透明且完全可审计的基于大模型的宏观金融压力测试管道，融合结构化提示与可选的国家基本面和新闻检索。系统为G7国家生成包含GDP增长、通货膨胀和政策利率的机器可读宏观经济情景，并通过因子映射转化为投资组合损失，实现相对于经典计量基准的置信度与预期亏损评估。在不同模型、国家及检索设置下，大模型生成连贯且具国别特征的压力叙事，尾部风险放大效果稳定，对检索选择不敏感。全面的合理性检查、情景诊断及方差分解显示，风险差异主要由投资组合构成和提示设计决定，而非检索机制。管道集成快照、确定性模式与哈希验证产物，确保可复现性与可审计性。结果表明，当结合透明结构与严格验证时，大模型生成的宏观情景可成为传统压力测试框架的可扩展、可解释补充。
原文摘要 · Abstract (English)
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.
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