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The Role of Time-Varying Rare Disaster Risks in Predicting Bond Returns and Volatility

Author

Listed:
  • Rangan Gupta

    (University of Pretoria, Pretoria, South Africa and IPAG Business School, Paris, France)

  • Tahir Suleman

    (School of Economics and Finance, Victoria University of Wellington, New Zealand and School of Business, Wellington Institute of Technology, New Zealand)

  • Mark E. Wohar

    (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)

Abstract

This paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles-based methodology, we show that rare disaster-risks affect only volatility, but not returns, of tenyear government bond of the US over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long-term government bonds of an alternative developed country (UK) and an emerging market (South Africa).

Suggested Citation

  • Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2017. "The Role of Time-Varying Rare Disaster Risks in Predicting Bond Returns and Volatility," Working Papers 201770, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201770
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    Cited by:

    1. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
    2. Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    3. Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, vol. 75(C).
    4. Su, Hao & Ying, Chengwei & Zhu, Xiaoneng, 2022. "Disaster risk matters in the bond market," Finance Research Letters, Elsevier, vol. 47(PA).

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    More about this item

    Keywords

    Bond Returns and Volatility; Rare Disasters; Nonparametric Quantile Causality;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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