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There is broad empirical evidence of regime switching in financial markets.The transition between different market regimes is mirrored in correlationmatrices, whose time-varying coefficients usually jump higher in highlyvolatile regimes, leading to the failure of common diversification methods. Inthis article, we aim to identify market regimes from covariance matrices anddetect transitions towards highly volatile regimes, hence improving tail-riskhedging. Starting from the time series of fractionally differentiatedsentiment-like future values, two models are applied on monthly realizedcovariance matrices to detect market regimes. Specifically, the regimedetection is implemented via vector logistic smooth transition autoregressivemodel (VLSTAR) and through an unsupervised learning methodology, theagglomerative hierarchical clustering. Since market regime switches areunobservable processes that describe the latent change of market behaviour, theability of correctly detecting market regimes is validated in two ways:firstly, randomly generated data are used to assess a correct classificationwhen regimes are known; secondly, a na\"{i}ve trading strategy filtered withthe detected regime switches is used to understand whether an improvement isshowed when accounting for regime switches. The results point to the VLSTAR asthe best performing model for labelling market regimes.

Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models