Do trading volumes explain the persistence of GARCH effects?

Rachael Carroll, Colm Kearney

    Research output: Contribution to journalArticleResearchpeer-review

    15 Citations (Scopus)

    Abstract

    We examine the role of trading volumes in Generalized Autoregressive Conditional Heteroscedasticity (GARCH)-based tests of the Mixture of Distributions Hypothesis (MDH) on firm-level data for the 20 largest Fortune 500 stocks. In doing so, we provide a set of increasingly generalized nested models within which to examine the role of trading volumes, beginning with the AR(1)-GARCH(1,1) model with no trading volumes, progressing to the univariate AR(1)-GARCH(1,1)-X, the AR(1)-GARCH(1,1)-M and the AR(1)-GARCH(1,1)-M-X models, the constant correlation bivariate AR(1)-GARCH(1,1)-M-X model, and culminating with the Dynamic Equicorrelation-GARCH (DECO-GARCH) model. Amongst our main findings, the trading volumes are robustly significant and positively signed in the volatility of returns equations for most firms, acting to reduce the persistence and to eliminate the need for GARCH terms. As we progress from the AR(1)-GARCH(1,1) to the AR(1)-GARCH(1,1)-X, AR(1)-GARCH(1,1)-M-X and the bivariate AR(1)-GARCH(1,1)-M-X models, the persistence parameters decline from an average of 0.987 to 0.143, 0.206 and 0.141 respectively. Our results are robust and consistent with the MDH in most cases.
    Original languageEnglish
    Pages (from-to)1993 - 2008
    Number of pages16
    JournalApplied Financial Economics
    Volume22
    Issue number23
    DOIs
    Publication statusPublished - 2012

    Cite this

    Carroll, Rachael ; Kearney, Colm. / Do trading volumes explain the persistence of GARCH effects?. In: Applied Financial Economics. 2012 ; Vol. 22, No. 23. pp. 1993 - 2008.
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    abstract = "We examine the role of trading volumes in Generalized Autoregressive Conditional Heteroscedasticity (GARCH)-based tests of the Mixture of Distributions Hypothesis (MDH) on firm-level data for the 20 largest Fortune 500 stocks. In doing so, we provide a set of increasingly generalized nested models within which to examine the role of trading volumes, beginning with the AR(1)-GARCH(1,1) model with no trading volumes, progressing to the univariate AR(1)-GARCH(1,1)-X, the AR(1)-GARCH(1,1)-M and the AR(1)-GARCH(1,1)-M-X models, the constant correlation bivariate AR(1)-GARCH(1,1)-M-X model, and culminating with the Dynamic Equicorrelation-GARCH (DECO-GARCH) model. Amongst our main findings, the trading volumes are robustly significant and positively signed in the volatility of returns equations for most firms, acting to reduce the persistence and to eliminate the need for GARCH terms. As we progress from the AR(1)-GARCH(1,1) to the AR(1)-GARCH(1,1)-X, AR(1)-GARCH(1,1)-M-X and the bivariate AR(1)-GARCH(1,1)-M-X models, the persistence parameters decline from an average of 0.987 to 0.143, 0.206 and 0.141 respectively. Our results are robust and consistent with the MDH in most cases.",
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    Do trading volumes explain the persistence of GARCH effects? / Carroll, Rachael; Kearney, Colm.

    In: Applied Financial Economics, Vol. 22, No. 23, 2012, p. 1993 - 2008.

    Research output: Contribution to journalArticleResearchpeer-review

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    AB - We examine the role of trading volumes in Generalized Autoregressive Conditional Heteroscedasticity (GARCH)-based tests of the Mixture of Distributions Hypothesis (MDH) on firm-level data for the 20 largest Fortune 500 stocks. In doing so, we provide a set of increasingly generalized nested models within which to examine the role of trading volumes, beginning with the AR(1)-GARCH(1,1) model with no trading volumes, progressing to the univariate AR(1)-GARCH(1,1)-X, the AR(1)-GARCH(1,1)-M and the AR(1)-GARCH(1,1)-M-X models, the constant correlation bivariate AR(1)-GARCH(1,1)-M-X model, and culminating with the Dynamic Equicorrelation-GARCH (DECO-GARCH) model. Amongst our main findings, the trading volumes are robustly significant and positively signed in the volatility of returns equations for most firms, acting to reduce the persistence and to eliminate the need for GARCH terms. As we progress from the AR(1)-GARCH(1,1) to the AR(1)-GARCH(1,1)-X, AR(1)-GARCH(1,1)-M-X and the bivariate AR(1)-GARCH(1,1)-M-X models, the persistence parameters decline from an average of 0.987 to 0.143, 0.206 and 0.141 respectively. Our results are robust and consistent with the MDH in most cases.

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