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Identification of structural multivariate GARCH models

Christian Hafner (), Helmut Herwartz and Simone Maxand

Journal of Econometrics, 2022, vol. 227, issue 1, 212-227

Abstract: The class of multivariate GARCH models is widely used to quantify and monitor volatility and correlation dynamics of financial time series. While many specifications have been proposed in the literature, these models are typically silent about the system inherent transmission of implied orthogonalized shocks to vector returns. In a framework of non-Gaussian independent structural shocks, this paper proposes a loss statistic, based on higher order co-moments, to discriminate in a data-driven way between alternative structural assumptions about the transmission scheme, and hence identify the structural model. Consistency of identification is shown theoretically and via a simulation study. In its structural form, a four dimensional system comprising US and Latin American stock market returns points to a substantial volatility transmission from the US to the Latin American markets. The identified structural model improves the estimation of classical measures of portfolio risk, as well as corresponding variations.

Keywords: Structural innovations; Identifying assumptions; MGARCH; Portfolio risk; Volatility transmission (search for similar items in EconPapers)
JEL-codes: C32 G15 (search for similar items in EconPapers)
Date: 2022
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Related works:
Working Paper: Identification of structural multivariate GARCH models (2020)
Working Paper: Identification of structural multivariate GARCH models (2018) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:1:p:212-227

DOI: 10.1016/j.jeconom.2020.07.019

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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