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Does the S&P500 index lead the crude oil dynamics? A complexity-based approach

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  • Kyrtsou, Catherine
  • Mikropoulou, Christina
  • Papana, Angeliki

Abstract

Taking the complex property of nonlinear feedback connectivity into consideration, the goal of this paper is to apprehend the interdependences between the financial and energy sectors. Our contribution is both theoretical and methodological. We conduct a multivariate analysis employing nonlinear tools, namely the Partial Transfer Entropy and the Asymmetric Mackey-Glass causality test. In particular, we build a system comprising the petroleum complex (crude oil, gasoline and heating oil), the S&P500 index and the 1-month futures-spot spread for crude oil. By adopting a rolling-window approach, we observe a persistent lead-lag relationship between the S&P500 index and the market participants' expectations for crude oil, from 2004 to 2009. Depending on the bubble period in the stock market, it appears that the resulting coupling becomes subject to the deterioration of global economic activity, induced by large common shocks.

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  • Kyrtsou, Catherine & Mikropoulou, Christina & Papana, Angeliki, 2016. "Does the S&P500 index lead the crude oil dynamics? A complexity-based approach," Energy Economics, Elsevier, vol. 56(C), pages 239-246.
    • Handle: RePEc:eee:eneeco:v:56:y:2016:i:c:p:239-246
    DOI: 10.1016/j.eneco.2016.02.001
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    1. Hao-Lin Shao & Ying-Hui Shao & Yan-Hong Yang, 2021. "New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach," Papers 2110.02693, arXiv.org.
    2. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Time-varying energy and stock market integration in Asia," Energy Economics, Elsevier, vol. 80(C), pages 777-792.
    3. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2021. "Hedging stocks with oil," Energy Economics, Elsevier, vol. 93(C).
    4. Mohammad Isleimeyyeh, 2020. "The role of financial investors in determining the commodity futures risk premium," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(9), pages 1375-1397, September.
    5. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2017. "Can stock market investors hedge energy risk? Evidence from Asia," Energy Economics, Elsevier, vol. 66(C), pages 559-570.
    6. KANAMURA Takashi, 2018. "Diversification Effect of Commodity Futures on Financial Markets," Discussion papers 18019, Research Institute of Economy, Trade and Industry (RIETI).
    7. Jin, Jiayu & Han, Liyan & Xu, Yang, 2022. "Does the SDR stabilize investing in commodities?," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 160-172.
    8. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2018. "Addressing COP21 using a stock and oil market integration index," Energy Policy, Elsevier, vol. 116(C), pages 127-136.
    9. Xiao, Di & Wang, Jun, 2020. "Dynamic complexity and causality of crude oil and major stock markets," Energy, Elsevier, vol. 193(C).
    10. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    11. Adams, Zeno & Collot, Solène & Kartsakli, Maria, 2020. "Have commodities become a financial asset? Evidence from ten years of Financialization," Energy Economics, Elsevier, vol. 89(C).

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    More about this item

    Keywords

    Nonlinear causality; S&P500; Petroleum complex; Futures-spot price spread; Speculation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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