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Causality in Quantiles and Dynamic Relations in Energy Markets

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  • Kyritsis, Evangelos
  • Andersson, Jonas

Abstract

In this paper we investigate the dynamic relations between crude oil price returns and a set of energy price returns, namely diesel, gasoline, heating, and the natural gas. This is performed by means of Granger non-causality tests for US spot closing prices over the period from January 1997 to December 2017. In previous studies this has been done by testing for the added predictive value of including lagged values of one energy price return in predicting the conditional expectation of another. In this paper, we instead focus on different ranges of the full conditional distribution within the framework of a dynamic quantile regression model, and identify the quantile ranges from which causality arises. The results constitute a richer set of findings than what is possible by just considering a single moment of the conditional distribution, which can be useful for implementing better substitution investment strategies and effective policy interventions. We find several interesting one-directional dynamic relations between the employed energy prices, especially in the tail quantiles, but also a bi-directional causal relation between energy prices for which the classical Granger non-causality test suggests otherwise. Our results are robust to alternative measures of the price of oil and different data frequencies.

Suggested Citation

  • Kyritsis, Evangelos & Andersson, Jonas, 2019. "Causality in Quantiles and Dynamic Relations in Energy Markets," Working Papers 116, VATT Institute for Economic Research.
    • Handle: RePEc:fer:wpaper:116
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    References listed on IDEAS

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    Cited by:

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    2. Yan, Wan-Lin & Cheung, Adrian (Wai Kong), 2025. "Quantile connectedness among climate policy uncertainty, news sentiment, oil and renewables in China," Research in International Business and Finance, Elsevier, vol. 76(C).
    3. Wang Gao & Jiajia Wei & Shixiong Yang, 2023. "The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    4. Halkos, George, 2020. "Examining the level of competition in the energy sector," MPRA Paper 98343, University Library of Munich, Germany.
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    6. Uribe, Jorge M. & Mosquera-López, Stephania & Arenas, Oscar J., 2022. "Assessing the relationship between electricity and natural gas prices in European markets in times of distress," Energy Policy, Elsevier, vol. 166(C).

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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