Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

Abstract

With Bitcoin, Ether and more than 2000 cryptocurrencies already forming a multi-billion dollar market, a proper understanding of their statistical and financial properties still remains elusive. Traditional economic theories do not explain their characteristics and standard financial models fail to capture their statistic and econometric attributes such as their extreme variability and heteroskedasticity. Motivated by these findings, we study Bitcoin and Ether prices via a Non-Homogeneous Pólya Gamma Hidden Markov (NHPG) model that has been shown to outperform its counterparts in conventional financial data. The NHPG algorithm has good in-sample performance and identifies both linear and non-linear effects of the predictors. Our results indicate that all price series are heteroskedastic with frequent changes between the two states of the underlying Markov process. In a somewhat unexpected result, the Bitcoin and Ether prices, although correlated, are significantly affected by different variables. We compare long term to short term Bitcoin data and find that significant covariates may change over time. Limitations of the current approach—as expressed by the large number of significant predictors and the poor out-of-sample predictions—back earlier findings that cryptocurrencies are unlike any other financial asset and hence, that their understanding requires novel tools and ideas.

Idiosyncrasies of Money: 21st Century Evolution of Money

“…Long-term Bitcoin data studies show a significant covariate change over time. Consequently, cryptocurrencies are unlike any other financial asset, and their understanding necessitates novel tools and approaches (Koki et al 2019).…”

Utilizando HMM para previsão de preço e estratégia de investimento em criptomoedas BitCoin

“…Nesta mesma linha, [Maupin 2019] publicou um trabalho em 2019, envolvendo a implementac ¸ão da abordagem Markov-Switching para prever o prec ¸o de quatro criptomoedas: Bitcoin, Ethereum, Ripple e Litecoin. Neste mesmo ano, [Koki et al 2019] propuseram realizar a previsão das criptomoedas Bitcoin e Ether através de um modelo oculto de markov nãohomogêneo, ou seja, modelo no qual a matriz de transic ¸ão estados do HMM não é constante ao longo do tempo. No ano seguinte, [Koki et al 2020], publicaram outro artigo na qual propuseram uma previsão dos valores do Bitcoin, Ethereum e Ripple com vários modelos ocultos de markov diferentes.…”

Trading Strategy for Market Situation Estimation Based on Hidden Markov Model

“…In 2019, Huang et al [17] studied the non-homogeneous hidden Markov model and proposed an improved EM algorithm to reveal the different patterns of bull and bear markets. Constandina et al [18] used HMM studying the statistical and financial properties of cryptocurrencies and analyzed their statistical properties. Eun-Chong Kim et al [19] used HMM to identify the phases of individual assets and proposed an investment strategy using price trends effectively.…”