Long-range memory, distributional variation and randomness of bitcoin volatility

Abstract

We investigate the nonlinear patterns of volatility in seven Bitcoin markets. In particular, we explore the fractional long-range dependence in conjunction with the potential inherent stochasticity of volatility time series under four diverse distributional assumptions, i.e., Normal, Student-t, Generalized Error (GED), and t-Skewed distribution. Our empirical findings signify the existence of long-range memory in Bitcoin market volatility, irrespectively of distributional inference. The same applies to entropy measurement, which indicates a high degree of randomness in the estimated series. As Bitcoin markets are highly disordered and risky, they cannot be considered suitable for hedging purposes. Our results provide strong evidence against the efficient market hypothesis.