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Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Author

Listed:
  • Mehmet Balcilar

    (EMU - Eastern Mediterranean University)

  • Elie Bouri

    (USEK - Université Saint-Esprit de Kaslik)

  • Rangan Gupta

    (University of Pretoria [South Africa])

  • David Roubaud

    (MRM - Montpellier Research in Management - UPVM - Université Paul-Valéry - Montpellier 3 - UPVD - Université de Perpignan Via Domitia - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM - Université de Montpellier)

Abstract

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns- except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.

Suggested Citation

  • Mehmet Balcilar & Elie Bouri & Rangan Gupta & David Roubaud, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Post-Print hal-02008551, HAL.
    • Handle: RePEc:hal:journl:hal-02008551
    DOI: 10.1016/j.econmod.2017.03.019
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    References listed on IDEAS

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    More about this item

    Keywords

    Volume; Bitcoin; C22; G15; Nonparametric quantile causality; Returns; Volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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