IDEAS home Printed from https://ideas.repec.org/a/taf/oaefxx/v8y2020i1p1719574.html
   My bibliography  Save this article

Adaptive market hypothesis: An empirical analysis of time –varying market efficiency of cryptocurrencies

Author

Listed:
  • Ambreen Khursheed
  • Muhammad Naeem
  • Sheraz Ahmed
  • Faisal Mustafa
  • David McMillan

Abstract

This study examines the adaptive market hypothesis (AMH) in relation to time-varying market efficiency by using three tests, namely Generalized Spectral (GS), Dominguez-Lobato (DL) and the automatic portmanteau test (AP) test on four-digital currencies; Bitcoin, Monaro, Litecoin, and Steller over the sample period of 2014–2018. The study applies Jarque-Bera test, ADF test, Ljung-Box statistics and ARCH-LM test for testing normality of returns, stationarity of series, serial correlation and volatility clustering in returns and squared returns of selected cryptocurrencies. Further, the study adopts an extremely important category of martingale difference hypothesis (MDH), which uses non-linear methods of dependencies for identifying changing linear and non-linear dependence in the price movement of currencies. The results indicate that price movements with linear and nonlinear dependences varies over time. Our tests also reveal that Bitcoin, Monaro and Litecoin have the longest efficiency periods. While Steller shows the longest inefficient market period. In view of varying market conditions, the results indicate that different market periods have significant impact on prices fluctuations of cryptocurrencies. Therefore, our findings suggest implementing the adaptive market hypothesis (AMH) as predicting changes in cryptocurrency prices over time must consider the time-varying market conditions for efficient forecasting.

Suggested Citation

  • Ambreen Khursheed & Muhammad Naeem & Sheraz Ahmed & Faisal Mustafa & David McMillan, 2020. "Adaptive market hypothesis: An empirical analysis of time –varying market efficiency of cryptocurrencies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1719574-171, January.
    • Handle: RePEc:taf:oaefxx:v:8:y:2020:i:1:p:1719574
    DOI: 10.1080/23322039.2020.1719574
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23322039.2020.1719574
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23322039.2020.1719574?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miklesh Prasad Yadav & Atul Kumar & Vidhi Tyagi, 2023. "Adaptive Market Hypothesis and Cointegration: An Evidence of the Cryptocurrency Market," Contemporary Studies in Economic and Financial Analysis, in: Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, volume 110, pages 27-43, Emerald Group Publishing Limited.
    2. 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.
    3. Misha Perepelitsa & Ilya Timofeyev, 2022. "Self-sustained price bubbles driven by digital currency innovations and adaptive market behavior," SN Business & Economics, Springer, vol. 2(3), pages 1-15, March.
    4. Perez Riaza, Baptiste & Gnabo, Jean-Yves, 2023. "Decentralized Autonomous Organizations (DAOs): Catalysts for enhanced market efficiency," Finance Research Letters, Elsevier, vol. 58(PB).

    More about this item

    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:taf:oaefxx:v:8:y:2020:i:1:p:1719574. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/OAEF20 .

    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.