IDEAS home Printed from https://ideas.repec.org/p/fer/wpaper/116.html
   My bibliography  Save this paper

Causality in Quantiles and Dynamic Relations in Energy Markets

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.doria.fi/handle/10024/168041
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kyritsis, Evangelos & Serletis, Apostolos, 2018. "The zero lower bound and market spillovers: Evidence from the G7 and Norway," Research in International Business and Finance, Elsevier, vol. 44(C), pages 100-123.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    4. Donald W. K. Andrews, 2003. "Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum," Econometrica, Econometric Society, vol. 71(1), pages 395-397, January.
    5. Atil, Ahmed & Lahiani, Amine & Nguyen, Duc Khuong, 2014. "Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices," Energy Policy, Elsevier, vol. 65(C), pages 567-573.
    6. Chang, Dongfeng & Serletis, Apostolos, 2018. "Oil, Uncertainty, And Gasoline Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 546-561, April.
    7. Bauwens, Luc & Peter Boswijk, H. & Urbain, Jean-Pierre, 2006. "Causality and exogeneity in econometrics," Journal of Econometrics, Elsevier, vol. 132(2), pages 305-309, June.
    8. Stephen P.A. Brown & Mine K. Yücel, 2008. "What Drives Natural Gas Prices?," The Energy Journal, , vol. 29(2), pages 45-60, April.
    9. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    10. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    11. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    12. Bacon, Robert W., 1991. "Rockets and feathers: the asymmetric speed of adjustment of UK retail gasoline prices to cost changes," Energy Economics, Elsevier, vol. 13(3), pages 211-218, July.
    13. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    14. Daskalaki, Charoula & Skiadopoulos, George, 2011. "Should investors include commodities in their portfolios after all? New evidence," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2606-2626, October.
    15. Ding, Haoyuan & Chong, Terence Tai-leung & Park, Sung Y., 2014. "Nonlinear dependence between stock and real estate markets in China," Economics Letters, Elsevier, vol. 124(3), pages 526-529.
    16. Severin Borenstein & A. Colin Cameron & Richard Gilbert, 1997. "Do Gasoline Prices Respond Asymmetrically to Crude Oil Price Changes?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(1), pages 305-339.
    17. Frank Asche & Petter Osmundsen & Maria Sandsmark, 2006. "The UK Market for Natural Gas, Oil and Electricity: Are the Prices Decoupled?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 27-40.
    18. Nathan S. Balke & Stephen P. A. Brown & Mine K. Yücel, 1998. "Crude oil and gasoline prices: an asymmetric relationship?," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q 1, pages 2-11.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jena, Sangram Keshari & Lahiani, Amine & Tiwari, Aviral Kumar & Roubaud, David, 2021. "Uncovering the complex asymmetric relationship between trading activity and commodity futures price: Evidenced from QNARDL study," Resources Policy, Elsevier, vol. 74(C).
    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.
    5. Huisman, Ronald & Stet, Cristian, 2022. "The dependence of quantile power prices on supply from renewables," Energy Economics, Elsevier, vol. 105(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kyritsis, Evangelos & Andersson, Jonas, 2019. "Causality in quantiles and dynamic relations in energy markets: (De)tails matter," Energy Policy, Elsevier, vol. 133(C).
    2. Scarcioffolo, Alexandre R. & Etienne, Xiaoli, 2021. "Testing directional predictability between energy prices: A quantile-based analysis," Resources Policy, Elsevier, vol. 74(C).
    3. Zhang, Tao & Ma, Guofeng & Liu, Guangsheng, 2015. "Nonlinear joint dynamics between prices of crude oil and refined products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 444-456.
    4. Bagnai, Alberto & Mongeau Ospina, Christian Alexander, 2018. "Asymmetries, outliers and structural stability in the US gasoline market," Energy Economics, Elsevier, vol. 69(C), pages 250-260.
    5. repec:cuf:journl:y:2017:v:18:i:1:dingfan is not listed on IDEAS
    6. Oladunjoye, Olusegun, 2008. "Market structure and price adjustment in the U.S. wholesale gasoline markets," Energy Economics, Elsevier, vol. 30(3), pages 937-961, May.
    7. Lahiani, Amine & Miloudi, Anthony & Benkraiem, Ramzi & Shahbaz, Muhammad, 2017. "Another look on the relationships between oil prices and energy prices," Energy Policy, Elsevier, vol. 102(C), pages 318-331.
    8. Łamasz Bartosz & Iwaszczuk Natalia & Ivashchuk Oleksandr, 2018. "Selected methods of securing the refining sector against crude oil price fluctuations," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 54(3), pages 197-209, September.
    9. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    10. Palazzi, Rafael Baptista & Meira, Erick & Klotzle, Marcelo Cabus, 2022. "The sugar-ethanol-oil nexus in Brazil: Exploring the pass-through of international commodity prices to national fuel prices," Journal of Commodity Markets, Elsevier, vol. 28(C).
    11. Hamid Baghestani & Jorg Bley, 2020. "Do directional predictions of US gasoline prices reveal asymmetries?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(2), pages 348-360, April.
    12. Khalid Khan & Jiluo Sun & Sinem Derindere Koseoglu & Ashfaq U. Rehman, 2021. "Revisiting Bitcoin Price Behavior Under Global Economic Uncertainty," SAGE Open, , vol. 11(3), pages 21582440211, August.
    13. Rebeca Jimenez-Rodriguez & Marcelo Sanchez, 2005. "Oil price shocks and real GDP growth: empirical evidence for some OECD countries," Applied Economics, Taylor & Francis Journals, vol. 37(2), pages 201-228.
    14. Karol Szomolanyi & Martin Lukacik & Adriana Lukacikova, 2022. "Estimation of asymmetric responses of U.S. retail fuel prices to changes in input prices based on a linear exponential adjustment cost approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 757-779, June.
    15. Mohammad Jaforullah & Alan King, 2015. "is New Zealand's economy vulnerable to world oil market shocks?," Working Papers 1503, University of Otago, Department of Economics, revised Mar 2015.
    16. Amano, R. A. & van Norden, S., 1998. "Oil prices and the rise and fall of the US real exchange rate," Journal of International Money and Finance, Elsevier, vol. 17(2), pages 299-316, April.
    17. Moutinho, Victor & Bento, João Paulo Cerdeira & Hajko, Vladimír, 2017. "Price relationships between crude oil and transport fuels in the European Union before and after the 2008 financial crisis," Utilities Policy, Elsevier, vol. 45(C), pages 76-83.
    18. Zhang, Wenbei & Qiu, Feng, 2024. "Rockets and Feathers in the Oil and Gasoline Markets: In-Depth Analysis of Three Asymmetries," 2024 Annual Meeting, July 28-30, New Orleans, LA 344062, Agricultural and Applied Economics Association.
    19. Kang, Wensheng & de Gracia, Fernando Perez & Ratti, Ronald A., 2019. "The asymmetric response of gasoline prices to oil price shocks and policy uncertainty," Energy Economics, Elsevier, vol. 77(C), pages 66-79.
    20. Kai-Hua Wang & Jia-Min Kan & Cui-Feng Jiang & Chi-Wei Su, 2022. "Is Geopolitical Risk Powerful Enough to Affect Carbon Dioxide Emissions? Evidence from China," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    21. N. Groenewold, 2000. "Financial Deregulation and the Relationship Between the Economy and the Share Market in Australia," Economics Discussion / Working Papers 00-10, The University of Western Australia, Department of Economics.
    22. Korbinian von Blanckenburg, Gerrit Reher, "undated". "Testverfahren zur Beurteilung der Funktionsfähigkeit von Marktprozessen," Working Papers 201154, Institute of Spatial and Housing Economics, Munster Universitary.

    More about this item

    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
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fer:wpaper:116. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anita Niskanen (email available below). General contact details of provider: https://edirc.repec.org/data/vatttfi.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.