Abstract
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.
Highlights
What are cryptocurrencies? How do they compare to traditional financial instruments? Are they like traditional money, like commodities, a hybrid of the former or an utterly new type of asset that merit their own definition and understanding? Early research, mainly focusing on Bitcoin ( BTC), provides mixed insights
Using GARCH models, Dyhrberg [4] demonstrates that BTC has similarities to both gold and the US dollar (USD) and somewhat surprisingly that it may be ideal for risk-averse investors
We report the in-sample performance for the logistic regression model in Table 3 for every threshold and for both cryptocurrencies
Summary
What are cryptocurrencies? How do they compare to traditional financial instruments? Are they like traditional money, like commodities, a hybrid of the former or an utterly new type of asset that merit their own definition and understanding? Early research, mainly focusing on Bitcoin ( BTC), provides mixed insights. Are they like traditional money, like commodities, a hybrid of the former or an utterly new type of asset that merit their own definition and understanding? While the creation of new BTCs resembles the mining process of gold—or precious metals in general—its attributes clearly differentiate it from conventional commodities [1]. The claim that BTC is fundamentally different from valuable metals like gold is backed by Klein et al [2] due to its shortage in stable hedging capabilities. Using data from a longer period (between 2010 and 2017), Demir et al [8] conclude the opposite, namely that BTC may serve as a hedging tool, due to its relationship to the Economic Policy Uncertainty Index (EUI)
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