IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v42y2017icp912-921.html
   My bibliography  Save this article

Evidences for a structural change in the oil market before a financial crisis: The flat horizon effect

Author

Listed:
  • Chiarucci, Riccardo
  • Loffredo, Maria I.
  • Ruzzenenti, Franco

Abstract

There are growing evidences that the commodity bubble in the 2000s had a major impact in the 2007–08 financial crisis. A salient feature of this commodity bubble was the dramatic increasing in the correlation of indexed commodities with oil price following the financialisation of the oil market. In this paper we suggest that, besides the growing demand from emerging economies and the following inflow of money from speculative traders, the introduction of the electronic platform could have had an important and underestimated effect on the oil market. Our analysis of the spot and futures oil prices at the NYMEX based on the Generalized Hurst Exponent confirms that the period 2004–2007 is pivotal in the oil market and corroborates the hypothesis that a structural change occurred in both markets. The evident decrease in multifractality suggests a flattening of the time horizon in financial oil markets and the coexistence of long-termism and short-termism. This structural change could partially explain the observed increase of correlations between commodities and oil price.

Suggested Citation

  • Chiarucci, Riccardo & Loffredo, Maria I. & Ruzzenenti, Franco, 2017. "Evidences for a structural change in the oil market before a financial crisis: The flat horizon effect," Research in International Business and Finance, Elsevier, vol. 42(C), pages 912-921.
    • Handle: RePEc:eee:riibaf:v:42:y:2017:i:c:p:912-921
    DOI: 10.1016/j.ribaf.2017.07.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531917301253
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2017.07.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Irwin, Scott H. & Sanders, Dwight R., 2012. "Financialization and Structural Change in Commodity Futures Markets," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(3), pages 371-396, August.
    2. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    3. Richard T. Baillie & Young‐Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long memory models for daily and high frequency commodity futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(7), pages 643-668, July.
    4. Lammerding, Marc & Stephan, Patrick & Trede, Mark & Wilfling, Bernd, 2013. "Speculative bubbles in recent oil price dynamics: Evidence from a Bayesian Markov-switching state-space approach," Energy Economics, Elsevier, vol. 36(C), pages 491-502.
    5. F. Pozzi & T. Matteo & T. Aste, 2012. "Exponential smoothing weighted correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(6), pages 1-21, June.
    6. Di Matteo, T. & Aste, T. & Dacorogna, M.M., 2003. "Scaling behaviors in differently developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 183-188.
    7. Sensoy, Ahmet & Hacihasanoglu, Erk & Nguyen, Duc Khuong, 2015. "Dynamic convergence of commodity futures: Not all types of commodities are alike," Resources Policy, Elsevier, vol. 44(C), pages 150-160.
    8. Owain ap Gwilym & Evamena Alibo, 2003. "Decreased price clustering in FTSE100 futures contracts following a transfer from floor to electronic trading," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(7), pages 647-659, July.
    9. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    10. James D. Hamilton, 2009. "Causes and Consequences of the Oil Shock of 2007-08," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 215-283.
    11. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
    12. Ke Tang & Wei Xiong, 2010. "Index Investment and Financialization of Commodities," NBER Working Papers 16385, National Bureau of Economic Research, Inc.
    13. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    14. Nicole M. Aulerich & Scott H. Irwin & Philip Garcia, 2014. "Bubbles, Food Prices, and Speculation: Evidence from the CFTC's Daily Large Trader Data Files," NBER Chapters, in: The Economics of Food Price Volatility, pages 211-253, National Bureau of Economic Research, Inc.
    15. Lutz Kilian & Bruce Hicks, 2013. "Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 385-394, August.
    16. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3218-3229.
    17. Adair Turner & Jon Farrimond & Jonathan Hill, 2011. "The oil trading markets, 2003--10: analysis of market behaviour and possible policy responses," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(1), pages 33-67, Spring.
    18. Gu, Rongbao & Chen, Hongtao & Wang, Yudong, 2010. "Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2805-2815.
    19. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    20. Dwight R. Sanders & Scott H. Irwin, 2010. "A speculative bubble in commodity futures prices? Cross‐sectional evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 25-32, January.
    21. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    22. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    23. Asako, Kazumi & Liu, Zhentao, 2013. "A statistical model of speculative bubbles, with applications to the stock markets of the United States, Japan, and China," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2639-2651.
    24. Kaufmann, Robert K., 2011. "The role of market fundamentals and speculation in recent price changes for crude oil," Energy Policy, Elsevier, vol. 39(1), pages 105-115, January.
    25. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2014. "Dependency structure and scaling properties of financial time series are related," LSE Research Online Documents on Economics 56622, London School of Economics and Political Science, LSE Library.
    26. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    27. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    28. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    29. Avalos, Fernando, 2014. "Do oil prices drive food prices? The tale of a structural break," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 253-271.
    30. Vansteenkiste, Isabel, 2011. "What is driving oil futures prices? Fundamentals versus speculation," Working Paper Series 1371, European Central Bank.
    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. Zhang, Yaxin & Shan, Yuli & Zheng, Xinzhu & Wang, Can & Guan, Yuru & Yan, Jin & Ruzzenenti, Franco & Hubacek, Klaus, 2023. "Energy price shocks induced by the Russia-Ukraine conflict jeopardize wellbeing," Energy Policy, Elsevier, vol. 182(C).
    2. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    3. Lin, Boqiang & Bai, Rui, 2021. "Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective," Research in International Business and Finance, Elsevier, vol. 56(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. Franco Ruzzenenti, 2015. "Changes in the relationship between the financial and real sector and the present economic financial crisis: study of energy sector and market," Working papers wpaper105, Financialisation, Economy, Society & Sustainable Development (FESSUD) Project.
    2. Das, Sougata & Kadapakkam, Palani-Rajan, 2020. "Machine over Mind? Stock price clustering in the era of algorithmic trading," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Yin, Libo & Zhou, Yimin, 2016. "What drives long-term oil market volatility? Fundamentals versus speculation," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-26.
    4. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    5. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    6. Robert J. Kauffman & Yuzhou Hu & Dan Ma, 2015. "Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-27, December.
    7. George Jiang & Ingrid Lo & Giorgio Valente, 2014. "High-Frequency Trading around Macroeconomic News Announcements: Evidence from the U.S. Treasury Market," Staff Working Papers 14-56, Bank of Canada.
    8. Corsetti, Giancarlo & Lafarguette, Romain & Mehl, Arnaud, 2019. "Fast trading and the virtue of entropy: evidence from the foreign exchange market," Working Paper Series 2300, European Central Bank.
    9. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    10. Mulligan, Robert F., 2017. "The multifractal character of capacity utilization over the business cycle: An application of Hurst signature analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 147-152.
    11. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    12. Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2023. "Arbitrage bots in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 262-278.
    13. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
    14. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
    15. Charfeddine, Lanouar, 2014. "True or spurious long memory in volatility: Further evidence on the energy futures markets," Energy Policy, Elsevier, vol. 71(C), pages 76-93.
    16. Foucault, Thierry & Moinas, Sophie, 2018. "Is Trading Fast Dangerous?," TSE Working Papers 18-881, Toulouse School of Economics (TSE).
    17. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    18. Benos, Evangelos & Brugler, James & Hjalmarsson, Erik & Zikes, Filip, 2017. "Interactions among High-Frequency Traders," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1375-1402, August.
    19. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).
    20. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2011. "A copula–multifractal volatility hedging model for CSI 300 index futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4260-4272.

    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:eee:riibaf:v:42:y:2017:i:c:p:912-921. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    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.