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Dual long-memory, structural breaks and the link between turnover and the range-based volatility


  • Karanasos, M.
  • Kartsaklas, A.


This paper investigates the issue of temporal ordering of the range-based volatility and turnover volume in the Korean market for the period 1995-2005. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish volume trading before the Asia financial crisis from trading after the crisis. We find that the apparent long-memory in the variables is quite resistant to the presence of breaks. However, when we take into account structural breaks the order of integration of the conditional variance series decreases considerably. Moreover, the impact of foreign volume on volatility is negative in the pre-crisis period but turns to positive after the crisis. This result is consistent with the view that foreign purchases tend to lower volatility in emerging markets--especially in the first few years after market liberalization when foreigners are buying into local markets--whereas foreign sales increase volatility. Before the crisis there is no causal effect for domestic volume on volatility whereas in the post-crisis period total and domestic volumes affect volatility positively. The former result is in line with the theoretical underpinnings that predict that trading within domestic investor groups does not affect volatility. The latter result is consistent with the theoretical argument that the positive relation between the two variables is driven by the uninformed general public.

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  • Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
  • Handle: RePEc:eee:empfin:v:16:y:2009:i:5:p:838-851

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    References listed on IDEAS

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    Cited by:

    1. Karanasos, Menelaos & Xu, Yongdeng, 2017. "Matrix Inequality Constraints for Vector (Asymmetric Power) GARCH/HEAVY Models and MEM with spillovers: some New (Mixture) Formulations," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    2. Rossi, Eduardo & Santucci de Magistris, Paolo, 2013. "Long memory and tail dependence in trading volume and volatility," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 94-112.
    3. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
    4. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179,
    5. Cevik, Emrah Ismail & Topaloğlu, Gültekin, 2014. "Volatilitede uzun hafıza ve yapısal kırılma: Borsa Istanbul örneği
      [Long memory and structural breaks on volatility: evidence from Borsa Istanbul]
      ," MPRA Paper 71485, University Library of Munich, Germany, revised 2014.
    6. Cleomar Gomes da Silva & Flávio Vilela Vieira, 2014. "BRICS countries: real interest rates and long memory," Economics Bulletin, AccessEcon, vol. 34(1), pages 409-419.
    7. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.


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