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...

This paper shows how the role of Financial Soundness Indicators (FSIs) in financial surveillance can be usefully enhanced. Drawing from different statistical techniques, the paper illustrates that FSIs generate signals that can accurately detect, with 4 to 12 quarters lead, emerging financial...

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast...

Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.Most traditional forecasting models rely on fitting data to a pre-specified relationship between inputand output variables, thereby assuming a specific functional and stochastic process underlying...

This paper describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning...

We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial,...

In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random...

We study the effects of a bank's engagement in trading. Traditional banking is relationship-based: not scalable, long-term oriented, with high implicit capital, and low risk (thanks to the law of large numbers). Trading is transactions-based: scalable, shortterm, capital constrained, and with...

This study assesses the overall impact on credit of the financial regulatory reforms in Europe, Japan, and the United States. Long-term cost estimates are provided for Basel III capital and liquidity requirements, derivatives reforms, and higher taxes and fees. Overall, average lending rates in...

The crisis in Europe has underscored the vulnerability of European bank funding models compared to international peers. This paper studies the drivers behind this fragility and examines the future of bank funding, primarily wholesale, in Europe. We argue that cyclical and structural factors have...