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Systemic Risk in the Global Energy Sector: Structure, Determinants and Portfolio Management Implications

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  • Syed Jawad Hussain Shahzad
  • Román Ferrer
  • Elie Bouri

Abstract

We examine the dynamics of tail dependence across returns of 105 global energy firms from 26 countries covering the regions of America, Asia Pacific and Europe. A partial correlation-based approach is used to quantify the dependence structure and level of systemic risk under relatively stable and extremely bearish and bullish market conditions. The dependence network of energy stock returns is constructed based on the novel triangulated maximally filtered graph (TMFG). The results reveal a high degree of tail dependence and role played by geographical proximity. The strongest links are found under extreme bearish market conditions. American and European energy firms are more interconnected and contribute more to systemic risk than Asian-Pacific companies. The dependence intensifies during periods of market turmoil, especially during the COVID-19 pandemic. A higher instability in the dependence structure is observed during extremely bearish market circumstances. A simple portfolio trading strategy based on the dependence ranking of energy firms outperforms a naïve equally-weighted buy-and-hold portfolio strategy.

Suggested Citation

  • Syed Jawad Hussain Shahzad & Román Ferrer & Elie Bouri, 2023. "Systemic Risk in the Global Energy Sector: Structure, Determinants and Portfolio Management Implications," The Energy Journal, , vol. 44(6), pages 211-243, November.
    • Handle: RePEc:sae:enejou:v:44:y:2023:i:6:p:211-243
    DOI: 10.5547/01956574.44.6.ssha
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    References listed on IDEAS

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    1. Jozef Baruník, Evzen Kocenda and Lukáa Vácha, 2015. "Volatility Spillovers Across Petroleum Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    2. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    3. Nikkinen, Jussi & Rothovius, Timo, 2019. "Energy sector uncertainty decomposition: New approach based on implied volatilities," Applied Energy, Elsevier, vol. 248(C), pages 141-148.
    4. Jozef Baruník & Evžen KoÄ enda b,a & Lukáš Vácha, 2016. "Volatility Spillovers Across Petroleum Markets," The Energy Journal, , vol. 37(1), pages 136-158, January.
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    Cited by:

    1. Joanna Górka & Katarzyna Kuziak, 2024. "Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach," Energies, MDPI, vol. 17(23), pages 1-29, November.

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