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Intraday jumps and trading volume: a nonlinear Tobit specification

Author

Listed:
  • Fredj Jawadi

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique, UEVE - Université d'Évry-Val-d'Essonne, LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management)

  • Waël Louhichi

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Abdoulkarim Idi Cheffou

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Rivo Randrianarivony

    (NIMEC - Normandie Innovation Marché Entreprise Consommation - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - IRIHS - Institut de Recherche Interdisciplinaire Homme et Société - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université)

Abstract

This paper investigates the relationship between trading volume and volatility for four international stock markets (US: S&P500, UK: FTSE100, France: CAC40 and Germany: DAX30) in a context of global financial crisis. Unlike previous related studies, we use intraday data and apply a nonlinear econometric model to assess this relationship. In particular, we first break down intraday realized volatility into its continuous and jump components using the non-parametric approach developed by Barndorff-Nielsen and Shephard (J Financ Econom 4:1–30, 2006). Second, we investigate the volume–volatility relationship and test whether it varies according to volatility components (jumps and continuous component). While Giot et al. (J Empir Finance 17:168–175, 2010), among others, investigated the volume–volatility relationship in a linear context, our study contributes by estimating different nonlinear specifications (threshold model, nonlinear Tobit model) that enable us to capture further asymmetry and time-variation to better apprehend the effect of trading volume on realized volatility. Accordingly, our study yields two interesting findings. On the one hand, as expected there is a significant and positive relationship between trading volume and realized volatility, as well as with its components, confirming the importance of trading volume as a key to characterizing volatility. On the other hand, we show that this relationship exhibits asymmetry and nonlinearity, and that threshold models are more appropriate than linear model to characterize the volume volatility relationship.

Suggested Citation

  • Fredj Jawadi & Waël Louhichi & Abdoulkarim Idi Cheffou & Rivo Randrianarivony, 2016. "Intraday jumps and trading volume: a nonlinear Tobit specification," Post-Print hal-02358454, HAL.
  • Handle: RePEc:hal:journl:hal-02358454
    DOI: 10.1007/s11156-015-0534-0
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    Cited by:

    1. 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.
    2. Xianfei Hui & Baiqing Sun & Hui Jiang & Indranil SenGupta, 2021. "Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters," Papers 2101.08984, arXiv.org, revised Feb 2022.
    3. Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2020. "Do Bitcoin and other cryptocurrencies jump together?," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 396-409.
    4. Jawadi, Fredj & Louhichi, Waël & Ameur, Hachmi Ben & Cheffou, Abdoulkarim Idi, 2016. "On oil-US exchange rate volatility relationships: An intraday analysis," Economic Modelling, Elsevier, vol. 59(C), pages 329-334.
    5. Jia Zhai & Yi Cao & Xuemei Ding, 2018. "Data analytic approach for manipulation detection in stock market," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 897-932, April.

    More about this item

    Keywords

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    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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