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COVID-19 spreading in financial networks: A semiparametric matrix regression model

Author

Listed:
  • Monica Billio

    (Department of Economics, Ca' Foscari University of Venice)

  • Roberto Casarin

    (Department of Economics, Ca' Foscari University of Venice)

  • Michele Costola

    (Department of Economics, Ca' Foscari University of Venice)

  • Matteo Iacopini

    (Vrije Universiteit Amsterdam)

Abstract

Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease.

Suggested Citation

  • Monica Billio & Roberto Casarin & Michele Costola & Matteo Iacopini, 2021. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Working Papers 2021:05, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2021:05
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    References listed on IDEAS

    as
    1. Monica Billio & Roberto Casarin & Matteo Iacopini & Sylvia Kaufmann, 2023. "Bayesian Dynamic Tensor Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 429-439, April.
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    4. Matthew Elliott & Benjamin Golub & Matthew O. Jackson, 2014. "Financial Networks and Contagion," American Economic Review, American Economic Association, vol. 104(10), pages 3115-3153, October.
    5. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
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    17. Monica Billio & Roberto Casarin & Matteo Iacopini, 2018. "Bayesian Markov Switching Tensor Regression for Time-varying Networks," Working Papers 2018:14, Department of Economics, University of Venice "Ca' Foscari".
    18. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
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    Cited by:

    1. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Jaipong, Peemmawat & Yamaka, Woraphon, 2022. "The transition of the global financial markets' connectedness during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    2. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2022. "Temporal networks in the analysis of financial contagion," Working Paper Series 2667, European Central Bank.
    3. Piribauer, Philipp & Glocker, Christian & Krisztin, Tamás, 2023. "Beyond distance: The spatial relationships of European regional economic growth," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).

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    More about this item

    Keywords

    Multilayer networks; financial markets; COVID-19;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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