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Oil Shocks and Volatility Jumps

Author

Listed:
  • Konstantinos Gkillas

    (Department of Business Administration , University of Patras, Patras, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • 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

In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in predicting volatility jumps in the S&P500 over the monthly period of 1988:01 to 2015:02, with the jumps having been computed based on daily data over the same period. Standard linear Granger causality test fail to detect any evidence of oil shocks causing volatility jumps. But given strong evidence of nonlinearity and structural breaks between jumps and oil shocks, we next used a nonparametric causality-in-quantiles test, since the linear model is misspecified. Using this data-driven robust approach, we were able to detect overwhelming evidence of oil shocks predicting volatility jumps of the S&P500 over its entire conditional distribution, with the strongest effect observed at the lowest considered conditional quantile. Interestingly, the predictive ability of the four oil shocks on volatility jumps are found to be both qualitatively and quantitatively similar.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Oil Shocks and Volatility Jumps," Working Papers 201825, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201825
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    Cited by:

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    2. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    3. Sheng, Xin & Gupta, Rangan & Ji, Qiang, 2020. "The impacts of structural oil shocks on macroeconomic uncertainty: Evidence from a large panel of 45 countries," Energy Economics, Elsevier, vol. 91(C).
    4. Konstantinos Gkillas & Rangan Gupta & Chi Keung Marco Lau & Muhammad Tahir Suleman, 2020. "Jumps beyond the realms of cricket: India's performance in One Day Internationals and stock market movements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(6), pages 1109-1127, April.
    5. Gkillas, Konstantinos & Konstantatos, Christoforos & Tsagkanos, Athanasios & Siriopoulos, Costas, 2021. "Do economic news releases affect tail risk? Evidence from an emerging market," Finance Research Letters, Elsevier, vol. 40(C).
    6. Gkillas, Konstantinos & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2022. "Spillovers in Higher-Order Moments of Crude Oil, Gold, and Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 398-406.
    7. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian & Yoon, Seong-Min, 2021. "OPEC news and jumps in the oil market," Energy Economics, Elsevier, vol. 96(C).
    8. Oladosu, Gbadebo, 2022. "Bubbles in US gasoline prices: Assessing the role of hurricanes and anti–price gouging laws," Journal of Commodity Markets, Elsevier, vol. 27(C).

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    More about this item

    Keywords

    S&P500; Volatility Jumps; Oil Shocks;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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