Granger-causal analysis of GARCH models: a Bayesian approach

Tomasz Woźniak

Department of Economics - Working Papers Series from The University of Melbourne

Abstract: A multivariate GARCH model is used to investigate Granger causality in the conditional variance of time series. Parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well, are derived. These novel conditions are convenient for the analysis of potentially large systems of economic variables. To evaluate hypotheses of noncausality, a Bayesian testing procedure is proposed. It avoids the singularity problem that may appear in theWald test and it relaxes the assumption of the existence of higher-order moments of the residuals required in classical tests.

Keywords: second-order noncausality; VAR-GARCH models; Bayesian hypotheses assessment (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 C53 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2015-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Granger-causal analysis of GARCH models: A Bayesian approach (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:mlb:wpaper:1194

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