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Testing Causality Between Two Vectors in Multivariate GARCH Models

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  • Tomasz Wozniak

Abstract

Spillover and contagion e ects have gained significant interest in the recent years of financial crisis. Attention has not only been directed to relations between returns of financial variables, but to spillovers in risk as well. I use the family of Constant Conditional Correlation GARCH models to model the risk associated with financial time series and to make inferences about Granger causal relations between second conditional moments. The restrictions for second-order Granger noncausality between two vectors of variables are derived. To assess the credibility of the noncausality hypotheses, I employ Bayes factors. Bayesian testing procedures have not yet been applied to the problem of testing Granger noncausality. Contrary to classical tests, Bayes factors make such testing possible, regardless of the form of the restrictions on the parameters of the model. Moreover, they relax the assumptions about the existence of higher-order moments of the processes required in classical tests.

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Bibliographic Info

Paper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 1139.

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Length: 28 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:mlb:wpaper:1139

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Keywords: Second-Order Causality; Volatility Spillovers; Bayes Factors; GARCH Models;

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  1. This Week's Reading
    by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2013-04-12 22:35:00
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Cited by:
  1. Matthieu Droumaguet & Tomasz Wozniak, 2012. "Bayesian Testing of Granger Causality in Markov-Switching VARs," Economics Working Papers ECO2012/06, European University Institute.
  2. Tomasz Wozniak, 2012. "Granger-causal analysis of VARMA-GARCH models," Economics Working Papers ECO2012/19, European University Institute.

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