This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the...

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the...

We study the economics and finance scholars' reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999-2016 period. We find...

We estimate new indices measuring financial and economic (in)stability in Austria and in the euro area. Instead of estimating the level of (in)stability in a financial or economic system we measure the degree of predictability of (in)stability, where our methodological approach is based on the...

MNB has received daily, transaction-level data on key Hungarian interest rate derivatives markets since the beginning of 2009 with the launching of the K14 report. The dataset that has accumulated since early 2009 provides an opportunity to better comprehend the structure and functioning of...

Banking crises can be extremely costly. The early detection of vulnerabilities can help prevent or mitigate those costs. We develop an early warning model of systemic banking crises that combines regression tree technology with a statistical algorithm (CRAGGING) to improve its accuracy and...

We set up an early warning system for financial crises based on the Random Forrest approach. We use a novel set of predictors that comprises financial development indicators (e.g. levels of credit to GDP ratio) in addition to conventional imbalances measures (e.g. credit gaps). The evaluation of...

Financial crises pose unique challenges for forecast accuracy. Using the IMF's Monitoring of Fund Arrangement (MONA) database, we conduct the most comprehensive evaluation of IMF forecasts to date for countries in times of crises. We examine 29 macroeconomic variables in terms of bias,...

We study the effects of stock market volatility on risk-taking and financial crises by constructing a cross-country database spanning up to 211 years and 60 countries. Prolonged periods of low volatility have strong in-sample and out-of-sample predictive power over the incidence of banking...

In this paper, we investigate the growing prominence of credit in the systemic banking crisis prediction literature. Through the application of the signal extraction model and multivariate probit panel regression, we evaluate the performance of the absolute change in credit-to-GDP ratio as an...