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Can spillover effects provide forecasting gains? The case of oil price volatility

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  • Chatziantoniou, Ioannis
  • Degiannakis, Stavros
  • Delis, Panagiotis
  • Filis, George

Abstract

We consider spillovers between oil price volatility and key uncertainty indicators. Adding to existing studies, we extend the applicability of the spillover index beyond economic inference, by generating forecasts of oil price volatility. Findings suggest that spillover effects do not contain significant predictive information. This in turn, raises critical questions regarding the usefulness of the spillover index for such task. However, it is critical to collect further evidence for the support of our findings.

Suggested Citation

  • Chatziantoniou, Ioannis & Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2019. "Can spillover effects provide forecasting gains? The case of oil price volatility," MPRA Paper 96266, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96266
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    References listed on IDEAS

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    1. Azzimonti, Marina, 2018. "Partisan conflict and private investment," Journal of Monetary Economics, Elsevier, vol. 93(C), pages 114-131.
    2. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    3. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    6. 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.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    9. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    10. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    11. Nikolaos Antonakakis & Ioannis Chatziantoniou & George Filis, 2014. "Dynamic Spillovers of Oil Price Shocks and Policy Uncertainty," Department of Economics Working Papers wuwp166, Vienna University of Economics and Business, Department of Economics.
    12. Kang, Wensheng & Perez de Gracia, Fernando & Ratti, Ronald A., 2017. "Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 344-359.
    13. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic spillovers of oil price shocks and economic policy uncertainty," Energy Economics, Elsevier, vol. 44(C), pages 433-447.
    14. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    15. Stavros Degiannakis & George Filis, 2019. "Forecasting European economic policy uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 94-114, February.
    16. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    17. Diaz, Elena Maria & de Gracia, Fernando Perez, 2017. "Oil price shocks and stock returns of oil and gas corporations," Finance Research Letters, Elsevier, vol. 20(C), pages 75-80.
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    More about this item

    Keywords

    Uncertainty; oil price volatility; forecasting; spillover effects;
    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
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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