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Compounding geopolitical and energy risks: A clustered stochastic multi-COVOL model

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  • Baltodano López, Ovielt
  • Billio, Monica
  • Casarin, Roberto
  • Costola, Michele

Abstract

This paper investigates the relationship between stock returns in the energy sector, energy uncertainty, and geopolitical risk. To this end, we propose a parsimonious and flexible model to extract common volatility factors (COVOL) from panel data. This general nonlinear multi-factor framework organizes panel units into groups based on different exposures to individual and compounding risks to reduce the number of parameters to be estimated. The group membership of the units is unknown, which naturally calls for using stochastic partition models. Random partition and compounding relationships are encoded in the weighted hyper-edges of a random hypergraph where the vertexes are the individual risks. In the empirical analysis, we study the volatility transmission in a multi-country setting and the role of individual and compounding risks.

Suggested Citation

  • Baltodano López, Ovielt & Billio, Monica & Casarin, Roberto & Costola, Michele, 2025. "Compounding geopolitical and energy risks: A clustered stochastic multi-COVOL model," Energy Economics, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325005274
    DOI: 10.1016/j.eneco.2025.108700
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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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