Working Paper: CEPR ID: DP18191
Authors: Maximilian Ahrens; Deniz Erdemlioglu; Michael McMahon; Christopher J. Neely; Xiye Yang
Abstract: Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers’ speeches. We create a novel dataset of US Federal Reserve speeches and use supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers’ speeches can help explain volatility and tail risk in both equity and bond markets. We also find that markets attend to these signals more closely during abnormal GDP and inflation regimes. Our results challenge the conventional view that central bank communication primarily resolves uncertainty.
Keywords: Central Bank Communication; Multimodal Machine Learning; Natural Language Processing; Speech Analysis; High Frequency Data; Volatility; Tail Risk
JEL Codes: E52; C45; C53; G12; G14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| central bankers' speeches (E58) | financial volatility (G19) |
| larger absolute forecast revision news (C53) | higher realized volatility in equity markets (G17) |
| speech-implied revisions to CPI forecasts (E31) | increased tail risk in equity markets (G19) |
| speech-implied revisions to unemployment forecasts (E27) | increased tail risk in equity markets (G19) |
| larger absolute forecast revision news (C53) | higher realized volatility in bond markets (G10) |
| larger absolute forecast revision news (C53) | increased tail risk in bond markets (G10) |
| central bankers' speeches (E58) | market reactions (during abnormal GDP and inflation regimes) (E32) |