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Conventional financial models fail to explain the economic and monetaryproperties of cryptocurrencies due to the latter's dual nature: their usage asfinancial assets on the one side and their tight connection to the underlyingblockchain structure on the other. In an effort to examine both components viaa unified approach, we apply a recently developed Non-Homogeneous Hidden Markov(NHHM) model with an extended set of financial and blockchain specificcovariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on theobservable series, the NHHM model offers a novel perspective on the underlyingmicrostructure of the cryptocurrency market and provides insight onunobservable parameters such as the behavior of investors, traders and miners.The algorithm identifies two alternating periods (hidden states) of inherentlydifferent activity -- fundamental versus uninformed or noise traders -- in theBitcoin ecosystem and unveils differences in both the short/long run dynamicsand in the financial characteristics of the two states, such as significantexplanatory variables, extreme events and varying series autocorrelation. In asomewhat unexpected result, the Bitcoin and Ether markets are found to beinfluenced by markedly distinct indicators despite their perceived correlation.The current approach backs earlier findings that cryptocurrencies are unlikeany conventional financial asset and makes a first step towards understandingcryptocurrency markets via a more comprehensive lens.

A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series