Interpreting the Interpreter: Can We Model post-ECB Conferences Volatility with LLM Agents?
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918508403621888 |
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| author | Collodel, Umberto |
| author_facet | Collodel, Umberto |
| contents | Central banks cannot observe market reactions to their communications before release. We propose a framework in which Large Language Models simulate 30 heterogeneous traders interpreting European Central Bank press conference transcripts, yielding a measure of cross-sectional disagreement among synthetic agents. Across 293 Governing Council events from 1998 to 2026, this measure correlates at approximately 0.5 with realized Overnight Index Swap volatility, outperforming standard text-based alternatives in explaining market reactions. LLM-implied disagreement adds information beyond volatility clustering and remains robust in out-of-sample validation on genuinely unseen conferences from January 2025 onwards. We further show that providing historical examples of pre and post-conference volatility improves the calibration of model responses. The framework offers a practical tool for assessing, prior to release, how central bank communication is likely to be interpreted by financial markets |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpreting the Interpreter: Can We Model post-ECB Conferences Volatility with LLM Agents? Collodel, Umberto General Economics Economics Central banks cannot observe market reactions to their communications before release. We propose a framework in which Large Language Models simulate 30 heterogeneous traders interpreting European Central Bank press conference transcripts, yielding a measure of cross-sectional disagreement among synthetic agents. Across 293 Governing Council events from 1998 to 2026, this measure correlates at approximately 0.5 with realized Overnight Index Swap volatility, outperforming standard text-based alternatives in explaining market reactions. LLM-implied disagreement adds information beyond volatility clustering and remains robust in out-of-sample validation on genuinely unseen conferences from January 2025 onwards. We further show that providing historical examples of pre and post-conference volatility improves the calibration of model responses. The framework offers a practical tool for assessing, prior to release, how central bank communication is likely to be interpreted by financial markets |
| title | Interpreting the Interpreter: Can We Model post-ECB Conferences Volatility with LLM Agents? |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2508.13635 |