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

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. Perez Riaza, Baptiste & Gnabo, Jean-Yves, 2023. "Decentralized Autonomous Organizations (DAOs): Catalysts for enhanced market efficiency," Finance Research Letters, Elsevier, vol. 58(PB).
    3. 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.
    4. 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.

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