We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870-2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

This paper investigates the power of macroeconomic factors to explain euro area bond risk premia using (i) a large dataset that captures the nowadays data-rich environment (ii) the Elastic Net variable selection. We find that macroeconomic factors, in particular economic activity and sentiment...

Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an...

The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional representation of a...

This paper develops a framework for assessing systemic risks and for predicting (out-of-sample) systemic events, i.e. periods of extreme financial instability with potential real costs. We test the ability of a wide range of “stand alone” and composite indicators in predicting systemic...

We assess the ability of yield curve factors to predict risk premia in short-term interest rates and exchange rates across a large sample of major advanced economies. We find that the same tick-shaped linear combination of (relative) bond yields predicts risk premia in both short-term interest...

This paper aims at providing a detailed analysis of the leading indicator properties of corporate bond spreads for real economic activity in the euro area. In- and out-of-sample predictive content of corporate bond spreads are examined along three dimensions: the bonds' quality, their term to...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

This paper proposes a general statistical framework for systemic financial stress indices which measure the severity of financial crises on a continuous scale. Several index designs from the financial stress and systemic risk literature can be represented as special cases. We introduce an...

This paper predicts phases of the financial cycle by combining a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals...