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Risk Aversion and Bitcoin Returns in Normal, Bull, and Bear Markets

Author

Listed:
  • Elie Bouri

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

  • Rangan Gupta

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

  • Chi Keung Marco Lau

    (Department of Accountancy, Finance and Economics, Huddersfield Business School, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK)

  • David Roubaud

    (Montpellier Business School, Montpellier, France)

Abstract

We study whether level of risk aversion can be used to predict Bitcoin returns. Using a copula-quantile approach, we find evidence of predictability for the lower and upper quantiles of the conditional distribution of returns (i.e., in bull and bear markets). To reveal the sign of the predictability, we apply the cross-quantilogram approach and find that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. In particular, we find positive predictability when the risk aversion is very low and at the medium level. However, the predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that very high levels of risk aversion are likely to drive down Bitcoin returns in a bull market.

Suggested Citation

  • Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud, 2019. "Risk Aversion and Bitcoin Returns in Normal, Bull, and Bear Markets," Working Papers 201927, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201927
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    References listed on IDEAS

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    2. Aharon, David Y. & Demir, Ender & Lau, Chi Keung Marco & Zaremba, Adam, 2022. "Twitter-Based uncertainty and cryptocurrency returns," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. Naeem, Muhammad Abubakr & Mbarki, Imen & Shahzad, Syed Jawad Hussain, 2021. "Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 496-514.

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    More about this item

    Keywords

    Risk-aversion; Bitcoin returns; price predictability; copulas; quantiles;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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