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Switching generalized autoregressive score copula models with application to systemic risk

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  • Mauro Bernardi
  • Leopoldo Catania

Abstract

Recent financial disasters have emphasized 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 patterns observed in financial time series. In this paper, we develop a new class of flexible copula models where the dependence parameters evolve according to a Markov switching generalized autoregressive score (GAS) dynamics. Maximum likelihood estimation is performed using a two‐step procedure where the second step relies on the expectation–maximization algorithm. The proposed switching GAS copula models are then used to estimate the conditional value at risk and the conditional expected shortfall, measuring the impact on an institution of extreme events affecting another institution or the market. The empirical investigation, conducted on a panel of European regional portfolios, reveals that the proposed model is able to explain and predict the evolution of the systemic risk contributions over the period 1999–2015.

Suggested Citation

  • Mauro Bernardi & Leopoldo Catania, 2019. "Switching generalized autoregressive score copula models with application to systemic risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 43-65, January.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:1:p:43-65
    DOI: 10.1002/jae.2650
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    Citations

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    Cited by:

    1. Psaradakis, Zacharias & Sola, Martin, 2024. "Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities," Econometrics and Statistics, Elsevier, vol. 29(C), pages 49-63.
    2. Umlandt, Dennis, 2023. "Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    4. Alanya-Beltran Willy, 2023. "Modelling volatility dependence with score copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 649-668, December.
    5. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    6. Tingguo Zheng & Hongyin Zhang & Shiqi Ye, 2024. "Monetary Policies on Green Financial Markets: Evidence from a Multi-Moment Connectedness Network," Papers 2405.02575, arXiv.org, revised Oct 2024.
    7. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    8. Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
    9. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    10. Ouyang, Ruolan & Zhuang, Chengkai & Wang, Tingting & Zhang, Xuan, 2022. "Network analysis of risk transmission among energy futures: An industrial chain perspective," Energy Economics, Elsevier, vol. 107(C).
    11. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Working Paper Series 2022-02, University of Trier, Research Group Quantitative Finance and Risk Analysis.
    12. Ortega-Jiménez, P. & Sordo, M.A. & Suárez-Llorens, A., 2021. "Stochastic orders and multivariate measures of risk contagion," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 199-207.
    13. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    14. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    15. Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
    16. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    17. Gavronski, Pedro Gerhardt & Ziegelmann, Flavio A., 2021. "Measuring systemic risk via GAS models and extreme value theory: Revisiting the 2007 financial crisis," Finance Research Letters, Elsevier, vol. 38(C).
    18. Dennis Umlandt, 2020. "Likelihood-based Dynamic Asset Pricing: Learning Time-varying Risk Premia from Cross-Sectional Models," Working Paper Series 2020-06, University of Trier, Research Group Quantitative Finance and Risk Analysis.

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