  In this paper, we consider a variety of multi-state Hidden Markov models for
predicting and explaining the Bitcoin, Ether and Ripple returns in the presence
of state (regime) dynamics. In addition, we examine the effects of several
financial, economic and cryptocurrency specific predictors on the
cryptocurrency return series. Our results indicate that the Non-Homogeneous
Hidden Markov (NHHM) model with four states has the best one-step-ahead
forecasting performance among all competing models for all three series. The
dominance of the predictive densities over the single regime random walk model
relies on the fact that the states capture alternating periods with distinct
return characteristics. In particular, the four state NHHM model distinguishes
bull, bear and calm regimes for the Bitcoin series, and periods with different
profit and risk magnitudes for the Ether and Ripple series. Also, conditionally
on the hidden states, it identifies predictors with different linear and
non-linear effects on the cryptocurrency returns. These empirical findings
provide important insight for portfolio management and policy implementation.
