Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Central bank communication between meetings often moves markets, but researchers have traditionally paid less attention to it. Using a dataset of U.S. Federal Reserve speeches, we develop supervised multimodal natural language processing methods to identify how monetary policy news affect bond and stock market volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that forecast revisions derived from FOMC-member speech can help explain volatility and tail risk in both equity and bond markets. Speeches from Chairs tend to produce larger forecast revisions and unconditionally raise volatility and tail risk, but their economic signals can calm markets (reduce volatility and tail risk). There is some evidence that a speaker’s monetary policy views may affect the impact of implied forecast revisions after conditioning on GDP growth.