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Switching-GAS Copula Models With Application to Systemic Risk

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  • Mauro Bernardi
  • Leopoldo Catania

Abstract

Recent financial disasters have emphasised the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and asymmetric dependence observed in financial returns. We develop a new class of flexible Copula models where the evolution of the dependence parameters follow a Markov-Switching Generalised Autoregressive Score (SGASC) dynamics. Maximum Likelihood estimation is consistently performed using the Inference Functions for Margins (IFM) approach and a version of the Expectation-Maximisation (EM) algorithm specifically tailored to this class of models. The SGASC models are then used to estimate the Conditional Value-at-Risk (CoVaR), which is defined as the VaR of a given asset conditional on another asset (or portfolio) being in financial distress, and the Conditional Expected Shortfall (CoES). Our empirical investigation shows that the proposed SGASC models are able to explain and predict the systemic risk contribution of several European countries. Moreover, we also find that the SGASC models outperform competitors using several CoVaR backtesting procedures.

Suggested Citation

  • Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
  • Handle: RePEc:arx:papers:1504.03733
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    3. Harvey, A. & Palumbo, D., 2021. "Regime switching models for directional and linear observations," Cambridge Working Papers in Economics 2123, Faculty of Economics, University of Cambridge.
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    5. Bernardi Mauro & Roy Cerqueti & Arsen Palestini, 2016. "Allocation of risk capital in a cost cooperative game induced by a modified Expected Shortfall," Papers 1608.02365, arXiv.org.

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