Bitcoin Cash: Stochastic Models of Fat-Tail Returns and Risk Modeling

Abstract

Bitcoin (BTC) is a digital currency that has gained significant attention from researchers. The aim of this paper consists in analyzing some stochastic models of fat-tail returns and risk models. The evidence of fat-tailed returns distribution for the BCH data is investigated, by performing a statistical analysis of Bitcoin Cash (BCH) in the U.S. dollar. By using daily Close, Open, Low, and High returns of BCH data series, the monthly-divided daily returns study describes further properties such as skewness, kurtosis, and correlation analysis. The results obtained prove that variance gamma distribution best fit the close, open and low returns, where high returns follow the generalized hyperbolic distribution. In addition, for the best-fitted fat-tailed returns distributions, several risk measures such as volatility, Value-at-Risk and Expected Shortfall measures are computed, analyzed and compared.