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Oil price shocks and the term structure of the US yield curve: a time–frequency analysis of spillovers and risk transmission

Author

Listed:
  • Zaghum Umar

    (Zayed University
    South Ural State University)

  • Mariya Gubareva

    (ISEG – Lisbon School of Economics & Management, Universidade de Lisboa
    SOCIUS/CSG - Research in Social Sciences and Management)

  • Tamara Teplova

    (National Research University Higher School of Economics/HSE University)

  • Wafa Alwahedi

    (Zayed University)

Abstract

This paper investigates the influence of oil demand, oil supply, and risk-driven shocks on the yield curve in the US between 1995 and 2020. The US term-structure shape is modeled by three structural factors, the level, slope, and curvature. Their empirical analysis is performed according to the Diebold-Li modified variant of the widely used Nelson-Siegel model. The technique of wavelet analysis allows investigating the interrelation of shocks in oil prices and the US yield curve along time and frequency domains, simultaneously. We report on low, medium, and high coherence zones, relative to the oil price movements and the changes in the three yield-curve factors. The low coherence intervals indicate the potential for the three latent factors to be used for creating diversification strategies capable of hedging adverse dynamics in the oil market, potentially workable through global crises. We document the variability of dynamic patterns observable for the US sovereign yield factors on per-type-of-shock basis, evidencing the potential role of the US sovereign debt investments for designing cross-asset hedge strategies for commodity and fixed-income markets.

Suggested Citation

  • Zaghum Umar & Mariya Gubareva & Tamara Teplova & Wafa Alwahedi, 2025. "Oil price shocks and the term structure of the US yield curve: a time–frequency analysis of spillovers and risk transmission," Annals of Operations Research, Springer, vol. 352(3), pages 363-387, September.
    • Handle: RePEc:spr:annopr:v:352:y:2025:i:3:d:10.1007_s10479-022-04786-1
    DOI: 10.1007/s10479-022-04786-1
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F3 - International Economics - - International Finance
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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