Author Description

We Introduce Unfear, Unsupervised Feature Extraction Clustering, To Identify Economic Crisis Regimes. Given Labeled Crisis And Non-crisis Episodes And The Corresponding Features Values, Unfear Uses Unsupervised Representation Learning And A Novel Mode Contrastive Autoencoder To Group Episodes Into Time-invariant Non-overlapping Clusters, Each Of Which Could Be Identified With A Different Regime. The Likelihood That A Country May Experience An Econmic Crisis Could Be Set Equal To Its Cluster Crisis Frequency. Moreover, Unfear Could Serve As A First Step Towards Developing Cluster-specific Crisis Prediction Models Tailored To Each Crisis Regime.