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Rare Disaster Risks and Volatility of the Term-Structure of US Treasury Securities: The Role of El Nino and La Nina Events

Author

Listed:
  • Renee van Eyden

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

  • Rangan Gupta

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

  • Jacobus Nel

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

  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

Abstract

The purpose of this paper is to determine the impact of rare disaster risks, captured by the El Nino-Southern Oscillation (ENSO) cycle, on the volatility of Treasury securities of the United States (US) involving 1- to 360-month maturities. We use a random coefficients panel-data-based heterogeneous autoregressive-realized variance (HAR-RV) model over the monthly period of 1961:06 to 2019:12, with the RV derived from the sum of squared daily changes in yield over a month. Our results show a positive and statistically significant (at the 1% level) impact of the ENSO cycle on RV, with the results being robust to alternative metrics of the ENSO, consideration of lagged impact, and decomposition of the ENSO cycle into El Nino and La Nina phases, with the former having a relatively stronger effect. With our panel estimation method using heterogeneous slope coefficients, we find that the effect on the entire term structure is positive, with higher impacts observed at the two-ends and the middle-part of the term-structure. Our results have important implications for investors in US Treasury securities.

Suggested Citation

  • Renee van Eyden & Rangan Gupta & Jacobus Nel & Elie Bouri, 2021. "Rare Disaster Risks and Volatility of the Term-Structure of US Treasury Securities: The Role of El Nino and La Nina Events," Working Papers 202155, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202155
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    References listed on IDEAS

    as
    1. Gupta, Rangan & Subramaniam, Sowmya & Bouri, Elie & Ji, Qiang, 2021. "Infectious disease-related uncertainty and the safe-haven characteristic of US treasury securities," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 289-298.
    2. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    3. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    4. Brian P. Poi, 2003. "From the help desk: Swamy's random-coefficients model," Stata Journal, StataCorp LP, vol. 3(3), pages 302-308, September.
    5. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    6. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    7. Riza Demirer & Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2020. "Effect of Rare Disaster Risks on Crude Oil: Evidence from El Nino from Over 140 Years of Data," Working Papers 2020104, University of Pretoria, Department of Economics.
    8. Liu, Yan & Wu, Jing Cynthia, 2021. "Reconstructing the yield curve," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1395-1425.
    9. Cashin, Paul & Mohaddes, Kamiar & Raissi, Mehdi, 2017. "Fair weather or foul? The macroeconomic effects of El Niño," Journal of International Economics, Elsevier, vol. 106(C), pages 37-54.
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    More about this item

    Keywords

    Rare Disaster Risks; ENSO Cycle; Term-Structure Volatility; US Treasury Securities; Panel HAR-RV Model;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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