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Dynamic large financial networks via conditional expected shortfalls

Giovanni Bonaccolto, Massimiliano Caporin () and Bertrand B. Maillet

European Journal of Operational Research, 2022, vol. 298, issue 1, 322-336

Abstract: In this article, we first generalize the Conditional Auto-Regressive Expected Shortfall (CARES) model by introducing the loss exceedances of all (other) listed companies in the Expected Shortfall related to each firm, thus proposing the CARES-X model (where the ‘X’, as usual, stands for eXtended in the case of large-dimensional problems). Second, we construct a regularized network of US financial companies by introducing the Least Absolute Shrinkage and Selection Operator in the estimation step. Third, we also propose a calibration approach for uncovering the relevant edges between the network nodes, finding that the estimated network structure dynamically evolves through different market risk regimes. We ultimately show that knowledge of the extreme risk network links provides useful information, since the intensity of these links has strong implications on portfolio risk. Indeed, it allows us to design effective risk management mitigation allocation strategies.

Keywords: Finance; Financial networks; Portfolio analysis; Systemic risk; Expectile regression (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:298:y:2022:i:1:p:322-336

DOI: 10.1016/j.ejor.2021.06.037

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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