IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v11y2022i6p350-361.html
   My bibliography  Save this article

Detecting multiple-equilibria and chaos in oil prices and global commodity markets

Author

Listed:
  • Ata Ozkaya

    (Assoc.Professor, Department of Economics, Galatasaray University, Çiragan Cad., No: 36, Ortaköy, 34647, Istanbul, Turkey)

Abstract

In the past 20 years, commodity markets have been examined under the hypothesis of whether the prices exhibit recurrent patterns or follow the stochastic processes. In this study, we analyze both global energy markets and food markets to detect the presence of multiple equilibria, which may lead to higher spikes in prices and to the occurrence of intermittency. This study focuses on daily prices in Brent petrol and Natural gas markets from 23 May 2017 to 23 May 2022. This time interval covers the period where extraordinary economic and financial policies have been preferred in countries worldwide amid the Covid-19 pandemic crisis. On the other hand, the study investigates global Food index monthly data from 2007M11 to 2022M4. Similarly, the time interval under examination points out the effects of the 2008 global crisis where expansionary monetary policies have been implemented both by the Federal Reserve of the U.S and the European Central Bank. We employed the phase-space reconstruction method for the Crude oil Brent Europe and spot Natural gas prices series, and global Food index data respectively. The main findings of the study demonstrate that commodity markets do not converge to a unique equilibrium level, instead multiple equilibria persist and chaotic behavior occurs. The presence of multiple equilibria leads to an increase in complexity and recurrently causes volatility in commodity markets, which may have spillover effects on other financial markets. Our results suggest that these effects simultaneously increase global inflationary pressures. From the perspective of policy making, it is crucial to establish a strategy to eliminate multiple equilibria and prevent high price spikes. Our findings have important implications for Central bank policies in emerging markets and portfolio and risk management. Key Words:multiple equilibria, intermittency, oil prices, food index, natural gas prices, chaos, market efficiency

Suggested Citation

  • Ata Ozkaya, 2022. "Detecting multiple-equilibria and chaos in oil prices and global commodity markets," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(6), pages 350-361, September.
    • Handle: RePEc:rbs:ijbrss:v:11:y:2022:i:6:p:350-361
    DOI: 10.20525/ijrbs.v11i6.1919
    as

    Download full text from publisher

    File URL: https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/1919/1417
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v11i6.1919
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v11i6.1919?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
    ---><---

    References listed on IDEAS

    as
    1. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    2. Saeed Moshiri & Faezeh Foroutan, 2006. "Forecasting Nonlinear Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 81-96.
    3. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    4. Gencay, Ramazan, 1998. "The predictability of security returns with simple technical trading rules," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 347-359, October.
    5. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
    6. Chen, Sheng-Tung & Kuo, Hsiao-I & Chen, Chi-Chung, 2010. "Modeling the relationship between the oil price and global food prices," Applied Energy, Elsevier, vol. 87(8), pages 2517-2525, August.
    7. John Kambhu & Neel Krishnan & Scott Weidman, 2007. "Part 1: Introduction to New Directions for Understanding Systemic Risk," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Nov), pages 3-14.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. John Kambhu & Neel Krishnan & Scott Weidman, 2007. "Preface to New Directions for Understanding Systemic Risk," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Nov).
    10. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    11. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2019. "A reappraisal of the chaotic paradigm for energy commodity prices," Energy Economics, Elsevier, vol. 82(C), pages 167-178.
    Full references (including those not matched with items on IDEAS)

    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. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    2. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    3. Loretta Mastroeni & Pierluigi Vellucci, 2017. "“Chaos” In Energy And Commodity Markets: A Controversial Matter," Departmental Working Papers of Economics - University 'Roma Tre' 0218, Department of Economics - University Roma Tre.
    4. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    5. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models," Papers 1401.1888, arXiv.org, revised Feb 2016.
    6. Yung-Ho Chang & Massoud Metghalchi & Chia-Chung Chan, 2006. "Technical trading strategies and cross-national information linkage: the case of Taiwan stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 731-743.
    7. Stefanescu, Răzvan & Dumitriu, Ramona, 2015. "Buy and sell signals on Bucharest Stock Exchange," MPRA Paper 89014, University Library of Munich, Germany, revised 05 Jan 2016.
    8. Yochanan Shachmurove & Uri BenZion & Paul Klein & Joseph Yagil, 2001. "A Moving Average Comparison of the Tel-Aviv 25 and S&P 500 Stock Indices," Penn CARESS Working Papers 4731f3394c43bebf4d3191c81, Penn Economics Department.
    9. Jacinta Chan Phooi M’ng & Rozaimah Zainudin, 2016. "Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    10. Bill Cai & Charlie Cai & Kevin Keasey, 2005. "Market Efficiency and Returns to Simple Technical Trading Rules: Further Evidence from U.S., U.K., Asian and Chinese Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(1), pages 45-60, March.
    11. Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Degang Wang, 2021. "Financial Markets Prediction with Deep Learning," Papers 2104.05413, arXiv.org.
    12. Kung, James J., 2009. "Predictability of Technical Trading Rules: Evidence from the Taiwan Stock Market," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 5(1-2), pages 1-17, March.
    13. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    14. Chen, Kuan-Hau & Su, Xuan-Qi & Lin, Li-Feng & Shih, Yi-Cheng, 2021. "Profitability of moving-average technical analysis over the firm life cycle: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    15. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
    16. Liu, Tie-Ying & Lee, Chien-Chiang, 2018. "Will the energy price bubble burst?," Energy, Elsevier, vol. 150(C), pages 276-288.
    17. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    18. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    19. Metghalchi, Massoud & Chang, Yung-Ho & Marcucci, Juri, 2008. "Is the Swedish stock market efficient? Evidence from some simple trading rules," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 475-490, June.
    20. Plantinga, Andrew J. & Provencher, Bill, 2001. "Internal Consistency In Models Of Optimal Resource Use Under Uncertainty," 2001 Annual meeting, August 5-8, Chicago, IL 20712, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    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:rbs:ijbrss:v:11:y:2022:i:6:p:350-361. 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: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.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.