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Adaptive market hypothesis and evolving predictability of bitcoin

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  • Khuntia, Sashikanta
  • Pattanayak, J.K.

Abstract

This study evaluates the adaptive market hypothesis (AMH) and evolving return predictability in bitcoin market. We use two robust methods in a rolling-window framework to capture time-varying linear and nonlinear dependence in bitcoin returns. We find that market efficiency evolves with time and validates the AMH in bitcoin market.

Suggested Citation

  • Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
  • Handle: RePEc:eee:ecolet:v:167:y:2018:i:c:p:26-28
    DOI: 10.1016/j.econlet.2018.03.005
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    References listed on IDEAS

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    1. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Persistence in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 46(C), pages 141-148.
    2. J. Carlos Escanciano & Ignacio N. Lobato, 2009. "Testing the Martingale Hypothesis," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 20, pages 972-1003, Palgrave Macmillan.
    3. Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
    4. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    5. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    6. Brauneis, Alexander & Mestel, Roland, 2018. "Price discovery of cryptocurrencies: Bitcoin and beyond," Economics Letters, Elsevier, vol. 165(C), pages 58-61.
    7. Manuel Dominguez & Ignacio Lobato, 2003. "Testing the Martingale Difference Hypothesis," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 351-377.
    8. Guglielmo Maria Caporale & Alex Plastun, 2019. "Price overreactions in the cryptocurrency market," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(5), pages 1137-1155, August.
    9. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    10. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    11. Gourishankar S Hiremath & Bandi Kamaiah, 2010. "Nonlinear Dependence in Stock Returns: Evidences from India," Journal of Quantitative Economics, The Indian Econometric Society, vol. 8(1), pages 69-85, January.
    12. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    13. Hiremath, Gourishankar S. & Narayan, Seema, 2016. "Testing the adaptive market hypothesis and its determinants for the Indian stock markets," Finance Research Letters, Elsevier, vol. 19(C), pages 173-180.
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    More about this item

    Keywords

    Adaptive market hypothesis (AMH); Bitcoin; Martingale difference hypothesis;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • 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

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