Dynamic large financial networks via conditional expected shortfalls. (English) Zbl 1490.91234

Summary: 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.


91G45 Financial networks (including contagion, systemic risk, regulation)
62P05 Applications of statistics to actuarial sciences and financial mathematics
91G10 Portfolio theory


erboost; glmnet; CAViaR
Full Text: DOI


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