IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v249y2025ipcs0304407624002720.html
   My bibliography  Save this article

Mind your language: Market responses to central bank speeches

Author

Listed:
  • Ahrens, Maximilian
  • Erdemlioglu, Deniz
  • McMahon, Michael
  • Neely, Christopher J.
  • Yang, Xiye

Abstract

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.

Suggested Citation

  • Ahrens, Maximilian & Erdemlioglu, Deniz & McMahon, Michael & Neely, Christopher J. & Yang, Xiye, 2025. "Mind your language: Market responses to central bank speeches," Journal of Econometrics, Elsevier, vol. 249(PC).
  • Handle: RePEc:eee:econom:v:249:y:2025:i:pc:s0304407624002720
    DOI: 10.1016/j.jeconom.2024.105921
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407624002720
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jeconom.2024.105921?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:249:y:2025:i:pc:s0304407624002720. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.