Abstract
Analogous to the way wind blows single grains of sand and the subsequent settling back atop sand dunes, we find statistical evidence to claim that the prices of cryptocurrencies exhibit similar unpredicted patterns, characterized by positive or negative jumps. Motivated by extant evidence of asset returns’ non-normality, we capture distributional properties of the log-returns of the Bitcoin and the following three cryptocurrencies in terms of market capitalization (Ethereum, Ripple and Bitcoin cash). The total error induced by the fitted distribution is remarkably decreased when the generalized hyperbolic distribution is used, a finding further validated by a series of goodness-of-fit type statistical tests. A complementary analysis for the foreign exchange market is conducted, with inherent similarities to that of cryptocurrencies. We reveal that the generalized hyperbolic distribution can also be used to model very widely traded currency pairs significantly more accurately than the log-normal.
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