A cryptocurrency empirical study focused on evaluating their distribution functions

Carmen López-Martín, Raquel Arguedas-Sanz and Sonia Benito Muela
Authors registered in the RePEc Author Service: Raquel Arguedas Sanz

International Review of Economics & Finance, 2022, vol. 79, issue C, 387-407

Abstract: This paper thoroughly examines the statistical properties of cryptocurrency returns, particularly focusing on studying which is the best statistical distribution for fitting this type of data. The preliminary statistical study reveals (i) high volatility, (ii) an inverse leverage effect, (iii) skewed distributions and (iv) high kurtosis. To capture the nonnormal characteristics observed in cryptocurrency data, we verified the goodness of fit of a large set of distributions, both symmetric and skewed distributions such as skewed Student-t, skewed generalized t, skewed generalized error and the inverse hyperbolic sign distributions. The results show that the skewed distributions outperform normal and Student-t distributions in fitting cryptocurrency data, although there is no one skewed distribution that systematically better fits the data. In addition, we compare these distributions in terms of their ability to forecast the market risk of cryptocurrencies. In line with the results obtained in the statistical analysis, we find that the skewed distributions provide better risk estimates than the normal and Student-t distributions, both in short and long positions, with SGED being the distribution that provides better results.

Keywords: Cryptocurrencies; Distributions; Skewness; Fat tail; Risk management (search for similar items in EconPapers)
JEL-codes: C46 C52 E17 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:79:y:2022:i:c:p:387-407

DOI: 10.1016/j.iref.2022.02.021

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