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Does Bitcoin Hedge Global Uncertainty? Evidence from Wavelet-Based Quantile-in-Quantile Regressions

Author

Listed:
  • Elie Bouri

    (USEK Business School, Holy Spirit University of Kaslik, Lebanon)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa.)

  • Aviral Kumar Tiwari

    (Center for Energy and Sustainable Development (CESD), Montpellier Business School, Montpellier, France)

  • David Roubaud

    (Center for Energy and Sustainable Development (CESD), Montpellier Business School, Montpellier Research in Management, Montpellier, France)

Abstract
In this study, we analyse whether Bitcoin can hedge uncertainty using daily data for the period of 17th March, 2011, to 7th October, 2016. Global uncertainty is measured by the first principal component of the VIXs of 14 developed and developing equity markets. We first use wavelets to decompose Bitcoin returns into various frequencies, i.e., investment horizons. Then, we apply standard OLS regressions and observe that uncertainty negatively affects raw Bitcoin return and its longer-term movements. However, given the heavy tails of the variables, we rely on quantile methods and reveal much more nuanced and interesting results. Quantile regressions indicate that Bitcoin does act as a hedge against uncertainty, that is, it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns. Finally, when we use quantile-on-quantile regressions, we observe that hedging is observed at shorter investment horizons, and at both lower and upper ends of Bitcoin returns and global uncertainty.

Suggested Citation

  • Elie Bouri & Rangan Gupta & Aviral Kumar Tiwari & David Roubaud, 2016. "Does Bitcoin Hedge Global Uncertainty? Evidence from Wavelet-Based Quantile-in-Quantile Regressions," Working Papers 201690, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201690
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bitcoin; global uncertainty; wavelet; quantile regressions;
    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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