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Mind Your Language: Market Responses to Central Bank Speeches

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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 develop 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. Our results challenge the conventional view that central bank communication primarily resolves uncertainty and indicate that markets attend to speech signals more closely during abnormal GDP and inflation regimes. Our analysis also reveals that the views of Fed members (i.e., hawkish versus dovish) tend to play a marginal role in terms of the strength of the speech signals. Looking at the speeches by the Fed Chair, we find that the Chair signals produce a larger tail risk compared to non-Chair signals, and the estimated magnitude of the market responses depends on the position of the officials (i.e., the Fed Chair or other Fed member).

Suggested Citation

  • Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers 2023-013, Federal Reserve Bank of St. Louis, revised 21 Feb 2024.
  • Handle: RePEc:fip:fedlwp:96270
    DOI: 10.20955/wp.2023.013
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    Cited by:

    1. Ehrmann, Michael & Gnan, Phillipp & Rieder, Kilian, 2023. "Central Bank Communication by ??? The Economics of Public Policy Leaks," CEPR Discussion Papers 18152, C.E.P.R. Discussion Papers.
    2. Cañon, Carlos & Gerba, Eddie & Pambira, Alberto & Stoja, Evarist, 2023. "An unconventional FX tail risk story," LSE Research Online Documents on Economics 120052, London School of Economics and Political Science, LSE Library.
    3. Carlos Cañon & Eddie Gerba & Alberto Pambira & Evarist Stoja, 2023. "An Unconventional FX Tail Risk Story," CESifo Working Paper Series 10629, CESifo.

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    More about this item

    Keywords

    central bank communication; multimodal machine learning; natural language processing; speech analysis; high-frequency data; volatility; tail risk;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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