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Threshold linkages between volatility and trading volume: evidence from developed and emerging markets

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  • Jawadi Fredj

    (University of Evry & France Business School - Campus Amiens, 2, rue Facteur Cheval, 91025 Evry, France)

  • Ureche-Rangau Loredana

    (Université de Picardie Jules Verne, CRIISEA, Pôle Universitaire Cathédrale, 10 placette Lafleur, BP 2716, 80027 Amiens Cedex 1, France)

Abstract

This paper studies volatility dynamics and provides further insights into its relationship with trading volume. In particular, we examine whether trading volume is significantly informative for investors when attempting to apprehend potential changes in volatility dynamics, and hence, in the evolution of market risk. To this end, we apply recent nonlinear modeling tools, namely Switching Transition Regression (STR) models that are robust to asymmetry and nonlinearity as well as TARCH models to check for the nature of transition between volatility regimes. Our findings show that volatility dynamics exhibit nonlinearity and switching regimes for which the transition is smooth rather than abrupt. Furthermore, one regime is associated with low volatility and a weak relationship with trading volume while in the second regime, the causality relationship is stronger and volatility is high. The paper’s novelty is to show that not only does trading volume contribute to explaining market volatility, but also that the change in volatility dynamics is performed through the change in its relationship with trading volume.

Suggested Citation

  • Jawadi Fredj & Ureche-Rangau Loredana, 2013. "Threshold linkages between volatility and trading volume: evidence from developed and emerging markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 313-333, May.
    • Handle: RePEc:bpj:sndecm:v:17:y:2013:i:3:p:313-333:n:2
    DOI: 10.1515/snde-2012-0040
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    2. Koubaa, Yosra & Slim, Skander, 2019. "The relationship between trading activity and stock market volatility: Does the volume threshold matter?," Economic Modelling, Elsevier, vol. 82(C), pages 168-184.
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    4. Roberts, Leigh, 2014. "Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns," Working Paper Series 18815, Victoria University of Wellington, School of Economics and Finance.
    5. Tobias R. Rühl & Michael Stein, 2014. "The impact of financial transaction taxes: Evidence from Italy," Economics Bulletin, AccessEcon, vol. 34(1), pages 25-33.
    6. Roberts, Leigh, 2014. "Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns," Working Paper Series 3169, Victoria University of Wellington, School of Economics and Finance.
    7. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.

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