  This paper studies of the multifractal dynamics in 84 cryptocurrencies. It
fills an important gap in the literature, by studying this market using two
alternative multi-scaling methodologies. We find compelling evidence that
cryptocurrencies have different degree of long range dependence, and --more
importantly -- follow different stochastic processes. Some of them follow
models closer to monofractal fractional Gaussian noises, while others exhibit
complex multifractal dynamics. Regarding the source of multifractality, our
results are mixed. Time series shuffling produces a reduction in the level of
multifractality, but not enough to offset it. We find an association of
kurtosis with multifractality.
