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
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Englisch
- Erschienen in
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Journal: Cogent Economics & Finance ; ISSN: 2332-2039 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-15 ; Abingdon: Taylor & Francis
- Klassifikation
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Wirtschaft
Asset Pricing; Trading Volume; Bond Interest Rates
Information and Market Efficiency; Event Studies; Insider Trading
International Financial Markets
- Thema
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Adaptive market hypothesis
digital currencies
market efficiency
- Ereignis
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Geistige Schöpfung
- (wer)
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Khursheed, Ambreen
Naeem, Muhammad
Ahmed, Sheraz
Mustafa, Faisal
- Ereignis
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Veröffentlichung
- (wer)
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Taylor & Francis
- (wo)
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Abingdon
- (wann)
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2020
- DOI
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doi:10.1080/23322039.2020.1719574
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:41 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Khursheed, Ambreen
- Naeem, Muhammad
- Ahmed, Sheraz
- Mustafa, Faisal
- Taylor & Francis
Entstanden
- 2020