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Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices

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  • Bouri, Elie
  • Gupta, Rangan
  • Lahiani, Amine
  • Shahbaz, Muhammad

Abstract

Unlike prior studies, this study examines the nonlinear, asymmetric and quantile effects of aggregate commodity index and gold prices on the price of Bitcoin. Using daily data from July 17, 2010 to February 2, 2017, we employed several advanced autoregressive distributed lag (ARDL) models. The nonlinear ARDL approach was applied to uncover short- and long-run asymmetries, whereas the quantile ARDL was applied to account for a second type of asymmetry, known as the distributional asymmetry according to the position of a dependent variable within its own distribution. Moreover, we extended the nonlinear ARDL to a quantile framework, leading to a richer new model, which allows testing for distributional asymmetry while accounting for short- and long-run asymmetries. Overall, our results indicate the possibility to predict Bitcoin price movements based on price information from the aggregate commodity index and gold prices. Importantly, we report the nuanced result that most often the relations between bitcoin and aggregate commodity, on the one hand, and between bitcoin and gold, on the other, are asymmetric, nonlinear, and quantiles-dependent, suggesting the need to apply non-standard cointegration models to uncover the complexity and hidden relations between Bitcoin and asset classes.

Suggested Citation

  • Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
  • Handle: RePEc:eee:jrpoli:v:57:y:2018:i:c:p:224-235
    DOI: 10.1016/j.resourpol.2018.03.008
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    References listed on IDEAS

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    1. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Benjamin M. Blau & Ryan J. Whitby, 2014. "Speculative Trading In Reits," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 37(1), pages 55-74, February.
    3. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    4. repec:eee:ecmode:v:64:y:2017:i:c:p:74-81 is not listed on IDEAS
    5. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    6. Aaron Yelowitz & Matthew Wilson, 2015. "Characteristics of Bitcoin users: an analysis of Google search data," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1030-1036, September.
    7. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    8. repec:taf:applec:v:49:y:2017:i:50:p:5063-5073 is not listed on IDEAS
    9. repec:eee:finlet:v:16:y:2016:i:c:p:85-92 is not listed on IDEAS
    10. Beat Weber, 2016. "Bitcoin and the legitimacy crisis of money," Cambridge Journal of Economics, Oxford University Press, vol. 40(1), pages 17-41.
    11. Jamal Bouoiyour & Refk Selmi, 2015. "What Does Bitcoin Look Like?," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 449-492, November.
    12. repec:eee:quaeco:v:66:y:2017:i:c:p:50-56 is not listed on IDEAS
    13. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
    14. C. Baek & M. Elbeck, 2015. "Bitcoins as an investment or speculative vehicle? A first look," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 30-34, January.
    15. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    16. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    17. Qiang Ji & Elie Bouri & Rangan Gupta & David Roubaud, 2017. "Network Causality Structures among Bitcoin and other Financial Assets: A Directed Acyclic Graph Approach," Working Papers 201729, University of Pretoria, Department of Economics.
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    Cited by:

    1. Afees A. Salisu & Wasiu Adekunle & Zachariah Emmanuel & Wasiu A. Alimi, 2018. "Predicting exchange rate with commodity prices: The role of structural breaks and asymmetries," Working Papers 055, Centre for Econometric and Allied Research, University of Ibadan.

    More about this item

    Keywords

    Cointegration; Asymmetry; Nonlinearity; Quantile dependence; Bitcoin; Commodity; Gold;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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