IDEAS home Printed from https://ideas.repec.org/p/cns/cnscwp/200306.html
   My bibliography  Save this paper

Oil and price dynamics in international petroleum markets

Author

Listed:
  • A. Lanza

    ()

  • M. Manera
  • M. Giovannini

Abstract

In this paper we investigate crude oil and products price dynamics. We present a comparison among ten prices series of crude oils and fourteen price series of petroleum products, considering four distinct market areas (Mediterranean, North Western Europe, Latin America and North America) over the period 1994-2002. We provide first a complete analysis of crude oil and product price dynamics using cointegration and error correction models. Subsequently we use the error correction specification to predict crude oil prices over the horizon January 2002-June 2002.The main findings of the paper can be summarized as follows - a) differences in quality are crucial to understand the behaviour of crudes; b) prices of crude oils whose physical characteristics are more similar to the marker show the following regularities - b1) they converge more rapidly to the long-run equilibrium; b2) there is an almost monotonic relation between Mean Absolute Percentage Error values and crude quality, measured by API° gravity and sulphur concentration; c) the price of the marker is the driving variable of the crude price also in the short-run, irrespective of the specific geographical area and the quality of the crude under analysis.

Suggested Citation

  • A. Lanza & M. Manera & M. Giovannini, 2003. "Oil and price dynamics in international petroleum markets," Working Paper CRENoS 200306, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:200306
    as

    Download full text from publisher

    File URL: https://crenos.unica.it/crenos/node/192
    Download Restriction: no

    File URL: https://crenos.unica.it/crenos/sites/default/files/wp/03-06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
    3. Asche, Frank & Gjolberg, Ole & Volker, Teresa, 2003. "Price relationships in the petroleum market: an analysis of crude oil and refined product prices," Energy Economics, Elsevier, vol. 25(3), pages 289-301, May.
    4. Gjolberg, Ole & Johnsen, Thore, 1999. "Risk management in the oil industry: can information on long-run equilibrium prices be utilized?," Energy Economics, Elsevier, vol. 21(6), pages 517-527, December.
    5. Adrangi, Bahram & Chatrath, Arjun & Raffiee, Kambiz & D. Ripple, Ronald, 2001. "Alaska North Slope crude oil price and the behavior of diesel prices in California," Energy Economics, Elsevier, vol. 23(1), pages 29-42, January.
    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. Mario Denni & G. Frewer, 2006. "New evidence on the relationship beetween crude oil and petroleum product prices," Departmental Working Papers of Economics - University 'Roma Tre' 0061, Department of Economics - University Roma Tre.

    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. Alessandro Lanza & Matteo Manera & Massimo Giovannini, 2003. "Oil and Product Price Dynamics in International Petroleum Markets," Working Papers 2003.81, Fondazione Eni Enrico Mattei.
    2. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    3. Masih, Mansur & Algahtani, Ibrahim & De Mello, Lurion, 2010. "Price dynamics of crude oil and the regional ethylene markets," Energy Economics, Elsevier, vol. 32(6), pages 1435-1444, November.
    4. 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.
    5. Toan Luu Duc Huynh & Muhammad Shahbaz & Muhammad Ali Nasir & Subhan Ullah, 0. "Financial modelling, risk management of energy instruments and the role of cryptocurrencies," Annals of Operations Research, Springer, vol. 0, pages 1-29.
    6. Hengyun Ma & Les Oxley & John Gibson, 2008. "Testing for Energy Market Integration in China," Working Papers in Economics 08/12, University of Canterbury, Department of Economics and Finance.
    7. Wlazlowski, Szymon & Giulietti, Monica & Binner, Jane & Milas, Costas, 2009. "Price dynamics in European petroleum markets," Energy Economics, Elsevier, vol. 31(1), pages 99-108, January.
    8. Westgaard, Sjur & Estenstad, Maria & Seim, Maria & Frydenberg, Stein, 2011. "Co-integration of ICE Gas oil and Crude oil futures," Energy Economics, Elsevier, vol. 33(2), pages 311-320, March.
    9. Hankyeung Choi & David J. Leatham & Kunlapath Sukcharoen, 2015. "Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 9(1), March.
    10. Ederington, Louis H. & Fernando, Chitru S. & Hoelscher, Seth A. & Lee, Thomas K. & Linn, Scott C., 2019. "A review of the evidence on the relation between crude oil prices and petroleum product prices," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 1-15.
    11. Asche, Frank & Misund, Bård & Sikveland, Marius, 2013. "The relationship between spot and contract gas prices in Europe," Energy Economics, Elsevier, vol. 38(C), pages 212-217.
    12. Lurion M. De Mello & Ronald D. Ripple, 2017. "Polypropylene Price Dynamics: Input Costs or Downstream Demand?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    13. Ma, Hengyun & Oxley, Les & Gibson, John, 2009. "Gradual reforms and the emergence of energy market in China: Evidence from tests for convergence of energy prices," Energy Policy, Elsevier, vol. 37(11), pages 4834-4850, November.
    14. 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.
    15. Herwartz, Helmut & Reimers, Hans-Eggert, 2006. "Modelling the Fisher hypothesis: World wide evidence," Economics Working Papers 2006-04, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Rodrigo Cerda & Alvaro Donoso & Aldo Lema, 2003. "Fundamentos del Tipo de Cambio Real en Chile," Documentos de Trabajo 244, Instituto de Economia. Pontificia Universidad Católica de Chile..
    17. Vo, D.H. & Nguyen, H.M. & Vo, A.T. & McAleer, M.J., 2019. "CO2 Emissions, Energy Consumption and Economic Growth," Econometric Institute Research Papers EI2019-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Yılmaz, Engin & Süslü, Bora, 2015. "The Calculation of Weighted Price Elasticity of Tax: Turkey (1998-2013)," MPRA Paper 64417, University Library of Munich, Germany, revised 15 Apr 2015.
    19. van Amano, Robert A & Norden, Simon, 1998. "Exchange Rates and Oil Prices," Review of International Economics, Wiley Blackwell, vol. 6(4), pages 683-694, November.
    20. Herzer, Dierk, 2020. "Semi-endogenous versus Schumpeterian growth models: a critical review of the literature and new evidence," MPRA Paper 100383, University Library of Munich, Germany.

    More about this item

    Keywords

    oil prices; product prices; error correction models;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General

    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:cns:cnscwp:200306. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CRENoS). General contact details of provider: http://edirc.repec.org/data/crenoit.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.