Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models

Constandina Koki, Stefanos Leonardos and Georgios Piliouras

Research in International Business and Finance, 2022, vol. 59, issue C

Abstract: 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.

Keywords: Cryptocurrencies; Bitcoin; Ether; Ripple; Hidden Markov models; Regime switching models; Bayesian inference; Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C52 E49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:59:y:2022:i:c:s0275531921001756

DOI: 10.1016/j.ribaf.2021.101554

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