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Panel evidence on the ability of oil returns to predict stock returns in the G7 area

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  • Westerlund, Joakim
  • Sharma, Susan Sunila

Abstract

Most studies on the ability of oil returns to predict stock returns use time series data for multiple countries. The data therefore have a panel structure. Yet, oddly enough, this structure is never really exploited in the estimation, but researchers tend to instead rely on country-by-country application of existing time series methods. This practice is wasteful, because it does not explore all available information. The present paper can be seen as a reaction to this. The purpose is to reevaluate the existing empirical evidence for the G7 countries using state-of-the-art panel data techniques. The results show that for the panel as a whole lagged oil price returns has a significantly negative effect on current stock returns. The evidence at the individual country level is less strong and varies depending on the extent to which the countries rely on oil import.

Suggested Citation

  • Westerlund, Joakim & Sharma, Susan Sunila, 2019. "Panel evidence on the ability of oil returns to predict stock returns in the G7 area," Energy Economics, Elsevier, vol. 77(C), pages 3-12.
  • Handle: RePEc:eee:eneeco:v:77:y:2019:i:c:p:3-12
    DOI: 10.1016/j.eneco.2018.05.007
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    Cited by:

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    2. Theophilus Teye Osah & Andre Varella Mollick, 2023. "Stock and oil price returns in international markets: Identifying short and long-run effects," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(1), pages 116-141, March.
    3. Maud Korley & Evangelos Giouvris, 2023. "Does Economic Policy Uncertainty Explain Exchange Rate Movements in the Economic Community of West African States (ECOWAS): A Panel ARDL Approach," IJFS, MDPI, vol. 11(4), pages 1-22, November.
    4. Xiao, Jihong & Wang, Yudong, 2022. "Good oil volatility, bad oil volatility, and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 953-966.
    5. Salah A. Nusair & Jamal A. Al-Khasawneh, 2023. "Changes in oil price and economic policy uncertainty and the G7 stock returns: evidence from asymmetric quantile regression analysis," Economic Change and Restructuring, Springer, vol. 56(3), pages 1849-1893, June.
    6. Wang, Lu & Ma, Feng & Niu, Tianjiao & He, Chengting, 2020. "Crude oil and BRICS stock markets under extreme shocks: New evidence," Economic Modelling, Elsevier, vol. 86(C), pages 54-68.
    7. Umar, Zaghum & Riaz, Yasir & Aharon, David Y., 2022. "Network connectedness dynamics of the yield curve of G7 countries," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 275-288.
    8. Kassouri, Yacouba & Altıntaş, Halil, 2020. "Commodity terms of trade shocks and real effective exchange rate dynamics in Africa's commodity-exporting countries," Resources Policy, Elsevier, vol. 68(C).
    9. Ovidijus Stauskas, 2023. "Complete Theory for CCE Under Heterogeneous Slopes and General Unknown Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 283-303, April.
    10. Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal, 2022. "Dependence between oil price changes and sectoral stock returns in Pakistan: Evidence from a quantile regression approach," Energy & Environment, , vol. 33(2), pages 315-331, March.

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    More about this item

    Keywords

    Predictive regression; Factor-augmented panel regression; CCE estimation; Interactive effects; Oil returns; Stock excess returns;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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