Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models
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 insight for portfolio management and policy implementation.
Keywords
Cite
@article{arxiv.2011.03741,
title = {Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models},
author = {Constandina Koki and Stefanos Leonardos and Georgios Piliouras},
journal= {arXiv preprint arXiv:2011.03741},
year = {2020}
}