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Threats to central bank independence and exchange rate volatility: High-frequency identification with Trump’s Fed tweets

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  • Liu, Yifan
  • Popova, Ivilina

Abstract

This study presents market-based evidence that threats to central bank independence increase exchange rate volatility. We use Trump’s tweets that criticize the Fed’s monetary policies and advocate lower interest rates as a proxy for threats to central bank independence. We find that intraday exchange rate volatility rises following Trump’s Fed tweets. Moreover, this relation is more pronounced for the tweets with greater market attention and more negative sentiment. Our findings suggest that market participants do not perceive the Federal Reserve as independent from political pressure.

Suggested Citation

  • Liu, Yifan & Popova, Ivilina, 2023. "Threats to central bank independence and exchange rate volatility: High-frequency identification with Trump’s Fed tweets," Finance Research Letters, Elsevier, vol. 53(C).
  • Handle: RePEc:eee:finlet:v:53:y:2023:i:c:s1544612323000156
    DOI: 10.1016/j.frl.2023.103641
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    1. Martin Eichenbaum & Charles L. Evans, 1995. "Some Empirical Evidence on the Effects of Shocks to Monetary Policy on Exchange Rates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(4), pages 975-1009.
    2. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    3. Carola Conces Binder, 2021. "Political Pressure on Central Banks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 715-744, June.
    4. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    5. van Binsbergen, Jules H. & Diamond, William F. & Grotteria, Marco, 2022. "Risk-free interest rates," Journal of Financial Economics, Elsevier, vol. 143(1), pages 1-29.
    6. Andrew J. Patton & Michela Verardo, 2012. "Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability," The Review of Financial Studies, Society for Financial Studies, vol. 25(9), pages 2789-2839.
    7. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    8. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    9. Daniel Andrei & Michael Hasler, 2015. "Investor Attention and Stock Market Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 33-72.
    10. Alesina, Alberto & Summers, Lawrence H, 1993. "Central Bank Independence and Macroeconomic Performance: Some Comparative Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(2), pages 151-162, May.
    11. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    12. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," The Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
    13. Bouakez, Hafedh & Normandin, Michel, 2010. "Fluctuations in the foreign exchange market: How important are monetary policy shocks?," Journal of International Economics, Elsevier, vol. 81(1), pages 139-153, May.
    14. repec:eid:wpaper:01/11 is not listed on IDEAS
    15. Feldmann, Horst, 2011. "The unemployment effect of exchange rate volatility in industrial countries," Economics Letters, Elsevier, vol. 111(3), pages 268-271, June.
    16. Kozo Kiyota & Shujiro Urata, 2004. "Exchange Rate, Exchange Rate Volatility and Foreign Direct Investment," The World Economy, Wiley Blackwell, vol. 27(10), pages 1501-1536, November.
    17. Aghion, Philippe & Bacchetta, Philippe & Rancière, Romain & Rogoff, Kenneth, 2009. "Exchange rate volatility and productivity growth: The role of financial development," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 494-513, May.
    18. Bianchi, Francesco & Kind, Thilo & Kung, Howard, 2019. "Threats to Central Bank Independence: High-Frequency Identification with Twitter," CEPR Discussion Papers 14021, C.E.P.R. Discussion Papers.
    19. Mohsen Bahmani‐Oskooee & Scott W. Hegerty, 2007. "Exchange rate volatility and trade flows: a review article," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 34(3), pages 211-255, August.
    20. Klomp, Jeroen & de Haan, Jakob, 2009. "Central bank independence and financial instability," Journal of Financial Stability, Elsevier, vol. 5(4), pages 321-338, December.
    21. Kris Boudt & James Thewissen, 2019. "Jockeying for Position in CEO Letters: Impression Management and Sentiment Analytics," Financial Management, Financial Management Association International, vol. 48(1), pages 77-115, March.
    22. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
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    Cited by:

    1. Zhou, Wen, 2023. "Did Donald Trump's tweets on Sino–U.S. Trade affect the offshore RMB exchange rate?," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Agrrawal, Pankaj & Agarwal, Rajat, 2023. "A Longer-Term evaluation of Information releases by Influential market Agents and the Semi-strong market Efficiency," EconStor Preprints 273555, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Central bank independence; Exchange rate volatility; High-frequency identification; Trump; Twitter; Social media;
    All these keywords.

    JEL classification:

    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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