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Magnitude and persistence of extreme risk spillovers in the global energy market: A high-dimensional left-tail interdependence perspective

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  • Zhu, Bo
  • Lin, Renda
  • Liu, Jiahao

Abstract

This paper studies the magnitude and persistence of extreme risk spillovers among stock returns of 124 energy companies worldwide between January 2006 and June 2019, as well as the corresponding firm-specific determinants of firms' extreme risk spillovers. The high-dimensional nonparametric method, the coefficient of tail interdependence (CTI), enables us to make better use of available information and to evaluate risk spillovers more precisely. Moreover, we use spatial panel models that consider spatial heterogeneity among firms to explore the determinants of the risk spillovers. The empirical findings from a study of energy firms' risk spillover effects verify the necessity of persistence measurement. Also, the most systemically risky energy companies do not necessarily have the largest firm sizes, while some relatively small firms can also generate high risk spillovers, which indicates that determinants other than firm size could also affect the spillover effect, such as business complexity and geographic location. Our regression results suggest that the extreme risk spillover of the energy companies is quite different in terms of business and region, which deserves more attention with respect to energy risk management. The estimates can be used for making portfolio decisions and designing regulatory policies.

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  • Zhu, Bo & Lin, Renda & Liu, Jiahao, 2020. "Magnitude and persistence of extreme risk spillovers in the global energy market: A high-dimensional left-tail interdependence perspective," Energy Economics, Elsevier, vol. 89(C).
  • Handle: RePEc:eee:eneeco:v:89:y:2020:i:c:s0140988320301018
    DOI: 10.1016/j.eneco.2020.104761
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    Cited by:

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    2. Chatziantoniou, Ioannis & Gabauer, David & Perez de Gracia, Fernando, 2022. "Tail risk connectedness in the refined petroleum market: A first look at the impact of the COVID-19 pandemic," Energy Economics, Elsevier, vol. 111(C).
    3. Dejan Živkov & Jasmina Đurašković & Marina Gajić‐Glamočlija, 2022. "Multiscale downside risk interdependence between the major agricultural commodities," Agribusiness, John Wiley & Sons, Ltd., vol. 38(4), pages 990-1011, October.
    4. Uddin, Gazi Salah & Luo, Tianqi & Yahya, Muhammad & Jayasekera, Ranadeva & Rahman, Md Lutfur & Okhrin, Yarema, 2023. "Risk network of global energy markets," Energy Economics, Elsevier, vol. 125(C).
    5. Zhu, Bo & Deng, Yuanyue & Lin, Renda & Hu, Xin & Chen, Pingshe, 2022. "Energy security: Does systemic risk spillover matter? Evidence from China," Energy Economics, Elsevier, vol. 114(C).

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    More about this item

    Keywords

    Energy firm; Extreme risk spillover; Interdependence; Persistence; Spatial heterogeneity;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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