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Variance (Non) Causality in Multivariate GARCH

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Author Info

  • Massimiliano Caporin

Abstract

This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH. By the introduction of a multiplicative causality impact function, the variance causality effects becomes directly interpretable and can therefore be used to detect both the existence of causality and its direction; notably, the proposed model allows for increasing and decreasing variance effects. An empirical application evidences negative causality effects between returns and volume of an Italian stock market index future contract.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930600972178
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 26 (2007)
Issue (Month): 1 ()
Pages: 1-24

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Handle: RePEc:taf:emetrv:v:26:y:2007:i:1:p:1-24

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Related research

Keywords: Multivariate GARCH; Variance causality; Volatility;

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Cited by:
  1. Yip, Iris W.H. & So, Mike K.P., 2009. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 327-340.
  2. Christian Conrad & Menelaos Karanasos, 2008. "Negative Volatility Spillovers in the Unrestricted ECCC-GARCH Model," KOF Working papers 08-189, KOF Swiss Economic Institute, ETH Zurich.
  3. PIERRET, Diane, 2013. "The systemic risk of energy markets," CORE Discussion Papers 2013018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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