This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model...

Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis...

We propose a new machine-learning-based approach for forecasting Value-at-Risk (VaR) named CoFiE-NN where a neural network (NN) is combined with Cornish-Fisher expansions (CoFiE). CoFiE-NN can capture non-linear dynamics of high-order statistical moments thanks to the flexibility of a NN while...

In this paper, we study systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 55 economies, and spanning the last three decades. Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy. A...

We update the widely used banking crises database by Laeven and Valencia (2008, 2010) with new information on recent and ongoing crises, including updated information on policy responses and outcomes (i.e. fiscal costs, output losses, and increases in public debt). We also update our dating of...

Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect...

Many empirical studies of banking crises have employed ""banking crisis"" (BC) indicators constructedusing primarily information on government actions undertaken in response to bank distress. Weformulate a simple theoretical model of a banking industry which we use to identify and...

This paper studies the effects of fiscal policy response in 118 episodes of systemic banking crisis in advanced and emerging market countries during 1980-2008. It finds that timely countercyclical fiscal measures contribute to shortening the length of crisis episodes by stimulating aggregate...

Did the occurrence of systemic banking crises in the 1990s and 2000s significantly alter the behavior of banks in the Mercosur? The objective of this paper is to answer this question by analyzing changes in bank behavior after crises in the Mercosur region. To our knowledge, this is the first...

This paper uses a Binary Classification Tree (BCT) model to analyze banking crises in 50 emerging market and developing countries during 1990-2005. The BCT identifies key indicators and their threshold values at which vulnerability to banking crisis increases. The three conditions identified as...