#3035. Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models

November 2026publication date
Proposal available till 30-05-2025
4 total number of authors per manuscript3510 $

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Journal’s subject area:
Business, Management and Accounting (miscellaneous);
Finance;
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Abstract:
The article analyzes a variety of hidden multi-state Markov models for predicting and explaining the returns of Bitcoin, Ethereum and Ripple in the presence of state dynamics (modes). The results show that the four-state heterogeneous hidden markov model (NHHM) has the best one-step-ahead prediction performance among all competing models for all three series.
Keywords:
Bayesian inference; Bitcoin; Cryptocurrencies; Ether; Forecasting; Hidden Markov models; Regime switching models; Ripple

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