用大模型动态识别市场状态，提升波动率预测精度。
Regime-aware financial volatility forecasting via in-context learning
- 通过上下文学习让大模型根据市场状态调整预测。
- 在高波动期表现显著优于传统方法和直接学习。
- 无需微调参数，适合实时金融风险预警场景。
本文提出一种基于上下文学习的制度感知框架，利用预训练大语言模型（LLM）在非平稳市场条件下进行金融波动率预测。该方法不需参数微调，通过历史波动模式推理并动态调整预测。我们设计了基于先验引导的精炼机制，从训练数据中构建与市场状态相关的示范样本。随后，将LLM作为上下文学习者，依据输入序列及条件采样的示范，预测下一步波动率。这种条件采样策略使模型仅通过上下文推理即可适应不同市场状态下的波动动态。在多个金融数据集上的实验表明，该框架在高波动时期显著优于经典波动率预测方法和直接的一次性学习，展现出更强的鲁棒性与适应性。
原文摘要 · Abstract (English)
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market conditions. The proposed approach deploys pretrained LLMs to reason over historical volatility patterns and adjust their predictions without parameter fine-tuning. We develop an oracle-guided refinement procedure that constructs regime-aware demonstrations from training data. An LLM is then deployed as an in-context learner that predicts the next-step volatility from the input sequence using demonstrations sampled conditional to the estimated market label. This conditional sampling strategy enables the LLM to adapt its predictions to regime-dependent volatility dynamics through contextual reasoning alone. Experiments with multiple financial datasets show that the proposed regime-aware in-context learning framework outperforms both classical volatility forecasting approaches and direct one-shot learning, especially during high-volatility periods.
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