Bitcoin cash: stochastic models of fat-tail returns and risk modeling

Author Affiliation

Muhammad Sheraz is Assistant Professor of Mathematical Sciences and Economics & Finance at Institute of Business Administration (IBA), Karachi

Faculty / School

Faculty of Computer Sciences (FCS)


Department of Mathematical Sciences

Was this content written or created while at IBA?


Document Type


Source Publication

Economic Computation and Economic Cybernetics Studies and Research




Applied Mathematics | Computer Sciences | Econometrics | Economics | Finance | Mathematics


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.

Indexing Information

HJRS - Y Category, Scopus, Web of Science - Social Sciences Citation Index (SSCI), Web of Science - Science Citation Index Expanded (SCI)

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