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

1
Department of Statistics, School of Information Sciences and Technology, Athens University of Economics and Business, 10434 Athens, Greece
2
Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372, Singapore
*
Author to whom correspondence should be addressed.
A preliminary version of this paper appeared Decentralized 2019 and awarded with the Best Paper Award.
Future Internet 2020, 12(3), 59; https://doi.org/10.3390/fi12030059
Received: 17 February 2020 / Revised: 18 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
We study the Bitcoin and Ether price series under a financial perspective. Specifically, we use two econometric models to perform a two-layer analysis to study the correlation and prediction of Bitcoin and Ether price series with traditional assets. In the first part of this study, we model the probability of positive returns via a Bayesian logistic model. Even though the fitting performance of the logistic model is poor, we find that traditional assets can explain some of the variability of the price returns. Along with the fact that standard models fail to capture the statistic and econometric attributes—such as extreme variability and heteroskedasticity—of cryptocurrencies, this motivates us to apply a novel Non-Homogeneous Hidden Markov model to these series. In particular, we model Bitcoin and Ether prices via the non-homogeneous Pólya-Gamma Hidden Markov (NHPG) model, since it has been shown that it outperforms its counterparts in conventional financial data. The transition probabilities of the underlying hidden process are modeled via a logistic link whereas the observed series follow a mixture of normal regressions conditionally on the hidden process. Our results show that the NHPG algorithm has good in-sample performance and captures the heteroskedasticity of both series. It identifies frequent changes between the two states of the underlying Markov process. In what constitutes the most important implication of our study, we show that there exist linear correlations between the covariates and the ETH and BTC series. However, only the ETH series are affected non-linearly by a subset of the accounted covariates. Finally, we conclude that the large number of significant predictors along with the weak degree of predictability performance of the algorithm back up earlier findings that cryptocurrencies are unlike any other financial assets and predicting the cryptocurrency price series is still a challenging task. These findings can be useful to investors, policy makers, traders for portfolio allocation, risk management and trading strategies. View Full-Text
Keywords: cryptocurrencies; bitcoin; ethereum; bayesian modeling; logistic regression; non-homogeneous hidden markov models; variables selection; forecasting cryptocurrencies; bitcoin; ethereum; bayesian modeling; logistic regression; non-homogeneous hidden markov models; variables selection; forecasting
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MDPI and ACS Style

Koki, C.; Leonardos, S.; Piliouras, G. Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach. Future Internet 2020, 12, 59. https://doi.org/10.3390/fi12030059

AMA Style

Koki C, Leonardos S, Piliouras G. Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach. Future Internet. 2020; 12(3):59. https://doi.org/10.3390/fi12030059

Chicago/Turabian Style

Koki, Constandina; Leonardos, Stefanos; Piliouras, Georgios. 2020. "Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach" Future Internet 12, no. 3: 59. https://doi.org/10.3390/fi12030059

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