Artikel

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

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.

Sprache
Englisch

Erschienen in
Journal: Cogent Economics & Finance ; ISSN: 2332-2039 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-15 ; Abingdon: Taylor & Francis

Klassifikation
Wirtschaft
Asset Pricing; Trading Volume; Bond Interest Rates
Information and Market Efficiency; Event Studies; Insider Trading
International Financial Markets
Thema
Adaptive market hypothesis
digital currencies
market efficiency

Ereignis
Geistige Schöpfung
(wer)
Khursheed, Ambreen
Naeem, Muhammad
Ahmed, Sheraz
Mustafa, Faisal
Ereignis
Veröffentlichung
(wer)
Taylor & Francis
(wo)
Abingdon
(wann)
2020

DOI
doi:10.1080/23322039.2020.1719574
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Khursheed, Ambreen
  • Naeem, Muhammad
  • Ahmed, Sheraz
  • Mustafa, Faisal
  • Taylor & Francis

Entstanden

  • 2020

Ähnliche Objekte (12)