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Granger-causal analysis of GARCH models: a Bayesian approach

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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.

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  • Tomasz Wozniak, 2015. "Granger-causal analysis of GARCH models: a Bayesian approach," Department of Economics - Working Papers Series 1194, The University of Melbourne.
  • Handle: RePEc:mlb:wpaper:1194
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    Keywords

    second-order noncausality; VAR-GARCH models; Bayesian hypotheses assessment;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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