Hidden Markov and regime switching copula models for state allocation in multiple time-series

We consider hidden Markov and regime-switching copula models as approaches for state allocation in multiple time-series, where state allocation means the prediction of the latent state characterizing each time occasion based on the observed data. This dynamic clustering, performed under the two model specifications, takes the correlation structure of the time-series into account. Maximum likelihood estimation of the model parameters is carried out by the expectation-maximization algorithm. For illustration we use data on the market of cryptocurrencies characterized by periods of high turbulence in which interdependence among assets is marked.