Abstract
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•We explore the predictive power of hidden Markov models in cryptocurrency returns.•The 4-state non-homogeneous hidden Markov model has the best forecasting performance.•Based on profits and risks, the model distinguishes bull, bear, and calm regimes.•The model identifies predictors with state-dependent, linear and non-linear effects.•The most common predictors are series momentum, VIX and US Treasury Yield.
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 benefits for portfolio management and policy implementation.