Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/326514 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Cogent Business & Management [ISSN:] 2331-1975 [Volume:] 11 [Issue:] 1 [Article No.:] 2394581 [Year:] 2024 [Pages:] 1-20
Verlag: 
Taylor & Francis, Abingdon
Zusammenfassung: 
This article investigates the time-series properties of cryptocurrency returns and compares them with currency and commodity returns. We perform and analyze the mean reversion, normality, unit root, high and low returns, correlation, Autoregressive Moving Average (ARMA) [2,2], Autoregressive (AR) [5], and long-run components in the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [1,1] estimates. We also perform regression analyses to evaluate two possible behavioral biases: familiarity and disposition effect. Our time series analysis documents that cryptocurrencies are neither currencies nor commodities. We also show that adding cryptocurrency to a portfolio increases market efficiency and uncertainty. We also document that cryptocurrency investors exhibit the same familiarity and disposition effect biases as commodity and currency investors. Overall, we conclude that investors in cryptocurrencies tend to underestimate risk and misestimate future prices, as they do in commodity and currency markets. This study makes at least three contributions to the literature. First, we evaluate whether cryptocurrencies tend to hedge or financialization. Second, our analysis includes both univariate and portfolio dimensions. Third, this is a pioneering study on using behavioral bias analysis to determine whether a cryptocurrency is a commodity or a currency.
Schlagwörter: 
Time-series
cryptocurrencies
ARMA
GARCH
behavioral bias
JEL: 
C22
G19
G40
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