  There is broad empirical evidence of regime switching in financial markets.
The transition between different market regimes is mirrored in correlation
matrices, whose time-varying coefficients usually jump higher in highly
volatile regimes, leading to the failure of common diversification methods. In
this article, we aim to identify market regimes from covariance matrices and
detect transitions towards highly volatile regimes, hence improving tail-risk
hedging. Starting from the time series of fractionally differentiated
sentiment-like future values, two models are applied on monthly realized
covariance matrices to detect market regimes. Specifically, the regime
detection is implemented via vector logistic smooth transition autoregressive
model (VLSTAR) and through an unsupervised learning methodology, the
agglomerative hierarchical clustering. Since market regime switches are
unobservable processes that describe the latent change of market behaviour, the
ability of correctly detecting market regimes is validated in two ways:
firstly, randomly generated data are used to assess a correct classification
when regimes are known; secondly, a na\"{i}ve trading strategy filtered with
the detected regime switches is used to understand whether an improvement is
showed when accounting for regime switches. The results point to the VLSTAR as
the best performing model for labelling market regimes.
