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Leverage effect in energy futures revisited*

* This paper is a replication of an original study

Author

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  • Carnero, M. Angeles
  • Pérez, Ana

Abstract

The objective of this paper is to replicate the results in Kristoufek (2014) on the leverage effect in energy futures and to analyze its robustness to both the methodology and the type of returns used. We first apply correlation-based tools for detecting both conditional heteroscedasticity and leverage effect. Then, we estimate asymmetric and long memory GARCH-type models using the data provided by Kristoufek (2014) by considering different software and the possibility that innovations follow a non-Gaussian distribution. Our findings confirm most of the results in the replicated paper. In particular, we can strongly confirm there is a significant leverage effect in the return series of WTI (West Texas Intermediate) and Brent crude oils. For the heating oil and the natural gas series, the statistical significance of the leverage effect depends on both the methodology and the type of returns used.

Suggested Citation

  • Carnero, M. Angeles & Pérez, Ana, 2019. "Leverage effect in energy futures revisited," Energy Economics, Elsevier, vol. 82(C), pages 237-252.
    • Handle: RePEc:eee:eneeco:v:82:y:2019:i:c:p:237-252
    DOI: 10.1016/j.eneco.2017.12.029
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    2. Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang, 2021. "Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 921-941, August.
    3. Pan, Qunxing & Sun, Yujia, 2023. "Changes in volatility leverage and spillover effects of crude oil futures markets affected by the 2022 Russia-Ukraine conflict," Finance Research Letters, Elsevier, vol. 58(PB).
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    Replication

    This item is a replication of:
  • Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.

    • More about this item

    Keywords

    Conditional heteroscedasticity; Quasi Maximum Likelihood; Robust estimators; TGARCH; EGARCH; FIEGARCH;
    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
    • 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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    1. Leverage effect in energy futures revisited (Energy Economics 2019) in ReplicationWiki

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