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Spatial Multivariate GARCH Models and Financial Spillovers

Author

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  • Rosella Giacometti

    (Department of Management, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy)

  • Gabriele Torri

    (Department of Management, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy
    Department of Finance, VŠB-TU Ostrava, Sokolská třída 33, 701 21 Ostrava, Czech Republic)

  • Kamonchai Rujirarangsan

    (Department of Economics, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy)

  • Michela Cameletti

    (Department of Economics, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy)

Abstract

We estimate the risk spillover among European banks from equity log-return data via Conditional Value at Risk (CoVaR). The joint dynamic of returns is modeled with a spatial DCC-GARCH which allows the conditional variance of log-returns of each bank to depend on past volatility shocks to other banks and their past squared returns in a parsimonious way. The backtesting of the resulting risk measures provides evidence that (i) the multivariate GARCH model with Student’s t distribution is more accurate than both the standard multivariate Gaussian model and the Filtered Historical Simulation (FHS), and (ii) the introduction of a spatial component improves the assessment of risk profiles and the market risk spillovers.

Suggested Citation

  • Rosella Giacometti & Gabriele Torri & Kamonchai Rujirarangsan & Michela Cameletti, 2023. "Spatial Multivariate GARCH Models and Financial Spillovers," JRFM, MDPI, vol. 16(9), pages 1-23, September.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:9:p:397-:d:1233944
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    References listed on IDEAS

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    2. Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024. "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, vol. 129(C).

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