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Oil price density forecasts: Exploring the linkages with stock markets

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  • Francesco Ravazzolo
  • Marco J. Lombardi

Abstract

In the recent years several commentators hinted at an increase of the correlation between equity and commodity prices, and blamed investment in commodity-related products for this. First, this paper investigates such claims by looking at various measures of correlation. Next, we assess to what extent correlations between oil and equity prices can be exploited for asset allocation. We develop a time-varying Bayesian Dynamic Conditional Correlation model for volatilities and correlations and find that joint modelling of oil and equity prices produces more accurate point and density forecasts for oil which lead to substantial benefits in portfolio wealth.

Suggested Citation

  • Francesco Ravazzolo & Marco J. Lombardi, 2012. "Oil price density forecasts: Exploring the linkages with stock markets," Working Papers No 3/2012, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    • Handle: RePEc:bny:wpaper:0008
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    Cited by:

    1. Balcilar, Mehmet & Katzke, Nico & Gupta, Rangan, 2017. "Do precious metal prices help in forecasting South African inflation?," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 63-72.
    2. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    3. Bianconi, Marcelo & Yoshino, Joe A., 2014. "Risk factors and value at risk in publicly traded companies of the nonrenewable energy sector," Energy Economics, Elsevier, vol. 45(C), pages 19-32.
    4. Wang, Shu & Zhou, Baicheng & Gao, Tianshu, 2023. "Speculation or actual demand? The return spillover effect between stock and commodity markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    5. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
    6. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    7. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    8. Yao, Wei & Alexiou, Constantinos, 2024. "On the transmission mechanism between the inventory arbitrage activity, speculative activity and the commodity price under the US QE policy: Evidence from a TVP-VAR model," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1054-1072.
    9. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.

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    More about this item

    Keywords

    Oil price; stock price; density forecasting; correlation; Bayesian DCC;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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