IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v167y2018icp26-28.html
   My bibliography  Save this article

Adaptive market hypothesis and evolving predictability of bitcoin

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176518300910
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2018.03.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    3. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    11. Brauneis, Alexander & Mestel, Roland, 2018. "Price discovery of cryptocurrencies: Bitcoin and beyond," Economics Letters, Elsevier, vol. 165(C), pages 58-61.
    12. 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.
    13. Manuel Dominguez & Ignacio Lobato, 2003. "Testing the Martingale Difference Hypothesis," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 351-377.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    2. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    3. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    4. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    5. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    6. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    7. Aslan, Aylin & Sensoy, Ahmet, 2020. "Intraday efficiency-frequency nexus in the cryptocurrency markets," Finance Research Letters, Elsevier, vol. 35(C).
    8. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    9. Khuntia, Sashikanta & Pattanayak, J.K., 2020. "Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume," Finance Research Letters, Elsevier, vol. 32(C).
    10. Majid Mirzaee Ghazani & Mohammad Ali Jafari, 2021. "Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    11. Rasim Özcan & Asad ul Islam KHAN & Sundas Iftikhar, 2024. "Whether The Cr Whether The Crypto Market Is Efficient? E et Is Efficient? Evidence F vidence From Testing The Validity Of The Efficient Market Hypothesis," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(1), pages 113-132, March.
    12. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    13. V Dimitrova & M Fernández-Martínez & M A Sánchez-Granero & J E Trinidad Segovia, 2019. "Some comments on Bitcoin market (in)efficiency," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
    14. Lennart Ante, 2020. "A place next to Satoshi: foundations of blockchain and cryptocurrency research in business and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1305-1333, August.
    15. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.
    16. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.
    17. Caporale, Guglielmo Maria & Kang, Woo-Young & Spagnolo, Fabio & Spagnolo, Nicola, 2021. "Cyber-attacks, spillovers and contagion in the cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    18. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    19. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    20. Wajdi Moussa & Nidhal Mgadmi & Rym Regaïeg & Abdelhafidh Othmani, 2020. "Non-linear adjustment of the Bitcoin–US dollar exchange rate," Digital Finance, Springer, vol. 2(1), pages 143-158, September.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:167:y:2018:i:c:p:26-28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.