Academic

Regime-aware financial volatility forecasting via in-context learning

arXiv:2603.10299v1 Announce Type: new Abstract: 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 app

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Saba Asaad, Shayan Mohajer Hamidi, Ali Bereyhi · · 1 min read · 2 views
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arXiv:2603.10299v1 Announce Type: new Abstract: 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.

Executive Summary

This article proposes a novel regime-aware in-context learning framework for financial volatility forecasting using large language models (LLMs). The framework leverages pretrained LLMs to reason over historical volatility patterns and adapt predictions without parameter fine-tuning. Experiments demonstrate superior performance compared to classical volatility forecasting approaches and direct one-shot learning, particularly during high-volatility periods. The framework's ability to adapt to regime-dependent volatility dynamics through contextual reasoning alone is a significant advancement in financial volatility forecasting. However, further research is needed to fully explore the potential of this framework and address its limitations.

Key Points

  • ▸ The proposed regime-aware in-context learning framework leverages LLMs for financial volatility forecasting under nonstationary market conditions.
  • ▸ The framework deploys pretrained LLMs to reason over historical volatility patterns and adjust predictions without parameter fine-tuning.
  • ▸ The conditional sampling strategy enables the LLM to adapt its predictions to regime-dependent volatility dynamics through contextual reasoning alone.

Merits

Strength in Adaptability

The framework's ability to adapt to regime-dependent volatility dynamics through contextual reasoning alone is a significant advancement in financial volatility forecasting.

Improved Performance

Experiments demonstrate superior performance compared to classical volatility forecasting approaches and direct one-shot learning, particularly during high-volatility periods.

Demerits

Limited Domain

The framework's performance may be limited to the specific financial datasets used in the experiments, and further research is needed to fully explore its potential in other domains.

Lack of Interpretability

The use of LLMs may make it difficult to interpret the results and understand the underlying reasoning behind the predictions.

Expert Commentary

The proposed regime-aware in-context learning framework is a notable advancement in financial volatility forecasting. The use of LLMs to reason over historical volatility patterns and adapt predictions without parameter fine-tuning is a significant improvement over classical volatility forecasting approaches. However, further research is needed to fully explore the potential of this framework and address its limitations. Additionally, the lack of interpretability of the results may be a concern for practitioners and policymakers. Nevertheless, the framework's potential to improve volatility forecasting and risk management makes it an exciting area of research.

Recommendations

  • ✓ Further research is needed to fully explore the potential of the regime-aware in-context learning framework and address its limitations.
  • ✓ The development of more interpretable machine learning frameworks is essential to facilitate the adoption of these models in real-world financial settings.

Sources

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