  Conventional financial models fail to explain the economic and monetary
properties of cryptocurrencies due to the latter's dual nature: their usage as
financial assets on the one side and their tight connection to the underlying
blockchain structure on the other. In an effort to examine both components via
a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov
(NHHM) model with an extended set of financial and blockchain specific
covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the
observable series, the NHHM model offers a novel perspective on the underlying
microstructure of the cryptocurrency market and provides insight on
unobservable parameters such as the behavior of investors, traders and miners.
The algorithm identifies two alternating periods (hidden states) of inherently
different activity -- fundamental versus uninformed or noise traders -- in the
Bitcoin ecosystem and unveils differences in both the short/long run dynamics
and in the financial characteristics of the two states, such as significant
explanatory variables, extreme events and varying series autocorrelation. In a
somewhat unexpected result, the Bitcoin and Ether markets are found to be
influenced by markedly distinct indicators despite their perceived correlation.
The current approach backs earlier findings that cryptocurrencies are unlike
any conventional financial asset and makes a first step towards understanding
cryptocurrency markets via a more comprehensive lens.

Econometrics;Statistical Finance