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Geopolitical Risks And The High-Frequency Movements Of The Us Term Structure Of Interest Rates

Author

Listed:
  • RANGAN GUPTA

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • ANANDAMAYEE MAJUMDAR

    (Inter-American Tropical Tuna Commission, 8901 La Jolla Shores Dr, La Jolla, CA 92037, USA)

  • JACOBUS NEL

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • SOWMYA SUBRAMANIAM

    (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)

Abstract

We use daily data for the period 25th November 1985 to 10th March 2020 to analyze the impact of newspapers-based measures of geopolitical risks (GPRs) on United States (US) Treasury securities by considering the level, slope and curvature factors derived from the term structure of interest rates of maturities covering 1 to 30 years. No evidence of predictability of the overall GPRs (or for threats and acts) is detected using linear causality tests. However, evidence of structural breaks and nonlinearity is provided by statistical tests performed on the linear model, which indicates that the Granger causality cannot be relied upon, as they are based on a misspecified framework. As a result, we use a data-driven approach, specifically a nonparametric causality-in-quantiles test, which is robust to misspecification due to regime changes and nonlinearity, to reconsider the predictive ability of the overall and decomposed GPRs on the three latent factors. Moreover, the zero lower bound situation, visible in our sample period, is captured by the lower quantiles, as this framework allows us to capture the entire conditional distribution of the three factors. Using this robust model, we find overwhelming evidence of causality from the GPRs, with relatively stronger effects from threats than acts, for the entire conditional distribution of the three factors, with higher impacts on medium- and long-run maturities, i.e., curvature and level factors, suggesting the predictability of the entire US term structure based on information contained in GPRs. Our results have important implications for academics, investors and policymakers.

Suggested Citation

  • Rangan Gupta & Anandamayee Majumdar & Jacobus Nel & Sowmya Subramaniam, 2021. "Geopolitical Risks And The High-Frequency Movements Of The Us Term Structure Of Interest Rates," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 1-16, September.
  • Handle: RePEc:wsi:afexxx:v:16:y:2021:i:03:n:s2010495221500123
    DOI: 10.1142/S2010495221500123
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    Cited by:

    1. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.

    More about this item

    Keywords

    Yield curve factors; geopolitical risks; causality-in-quantiles test;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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