Monetary Policy Shocks: A New Hope. Large Language Models and Central Bank Communication

Rubén Fernández-Fuertes

No 25257, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy

Abstract: I develop a multi-agent LLM framework that processes Federal Reserve communications to construct narrative monetary policy surprises. By analyzing Beige Books and Minutes released before each FOMC meeting, the system generates conditional expectations that yield less noisy surprises than market-based measures. These surprises produce theoretically consistent impulse responses where contractionary shocks generate persistent disinflationary effects and enable profitable yield curve trading strategies that outperform alternatives. By directly extracting expectations rather than cleaning surprises ex post, this approach demonstrates how multi-agent LLMs can implement narrative identification at scale without contamination in high-frequency measures.

Keywords: Monetary Policy Shocks; Central Bank Communication; Large Language Models; FOMC; Federal Reserve; Natural Language Processing; High-Frequency Identification; Term Structure (search for similar items in EconPapers)
JEL-codes: C45 C55 E43 E52 E58 G14 (search for similar items in EconPapers)
Pages: 80
Date: 2025
New Economics Papers: this item is included in nep-big, nep-cba and nep-mon
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