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Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential

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  • Naqvi, Bushra
  • Mirza, Nawazish
  • Umar, Muhammad
  • Rizvi, Syed Kumail Abbas

Abstract

Amidst the rise of Shanghai crude oil futures (SCOF) as a preeminent contender in the global oil arena, this study analyzes its returns and volatility structures in compelling contrast to West Texas Intermediate (WTI) and Brent oil futures (BRENT). A comprehensive examination is conducted using various GARCH models and News impact curves, with the analysis based on daily data spanning from April 2021 to March 2023. The results reveal distinct responses of SCOF when contrasted with WTI and Brent. Firstly, the study finds that SCOF returns exhibit a level of independence from global market movements. Secondly, the assessment of potential asymmetries in the volatility structures displays notable differences among the three markets. Specifically, WTI demonstrates the highest asymmetry, while SCOF exhibits lower asymmetry. These findings imply that SCOF returns exhibit stability and resilience and hold the potential to serve as a formidable hedge against adverse shocks. As investors and policymakers navigate the complex terrain of the global oil market, these insights underscore the strategic advantages and opportunities that SCOF may offer, both in individual investment decisions and broader risk management strategies.

Suggested Citation

  • Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad & Rizvi, Syed Kumail Abbas, 2023. "Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential," Energy Economics, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006084
    DOI: 10.1016/j.eneco.2023.107110
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    More about this item

    Keywords

    SCOF; WTI; Brent; Volatility dynamics; Crude oil futures;
    All these 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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