Spatial Multivariate GARCH Models and Financial Spillovers

Rosella Giacometti, Gabriele Torri (), Kamonchai Rujirarangsan and Michela Cameletti
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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
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

JRFM, 2023, vol. 16, issue 9, 1-23

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.

Keywords: spatial multivariate GARCH; spatial weights; CoVaR (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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