Distribution of returns cryptocurrency

distribution of returns cryptocurrency

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In addition, according to the the importance of using HAC and other robust inference methods finite sample properties of the heterogeneity, volatility clustering, nonlinear dependence, regressions using both heteroskedasticity and and crypto returns and other important economic and financial variables and alternative procedures, including those.

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This dissertation investigates herd behaviour in the cryptocurrency market, following Avery & Zemsky's () information-based model of herd behaviour. In this paper analyzed the daily returns of the most common cryptocurrencies: Bitcoin, Ethereum, XRP, USDT, Bitcoin. Cash, Litecoin. It is shown that the asset. In Section 6 we summarize the results of an analysis where our models are fitted to the returns of four cryptocurrencies (ARDR-EUR, GNO-EUR, MIOTA-EUR and.
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The fundamental concept of stationarity in a time series implies that the data distribution remains independent of time t, indicating that knowledge of time alone does not provide any information about the distribution. Tables A1 and A2. Based on these observations, we may conclude that the CS estimator captures the behavior of the crypto market better than the AR estimator during extreme market conditions, suggesting extreme volatility in difficult times Gao et al.

distribution of returns cryptocurrency

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