We develop a stock-flow-consistent agent-based model that comprises a realistic mechanism of money creation and parametrize it to fit actually observed data. The model is used to make out-of-sample projections of broad money and credit developments under the commencement/termination of foreign...

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to...

This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coeffcients under a correlated random effects distribution. This...

Using a large panel of US banks over the period 2008-2013, this paper proposes an early warning framework to identify bank heading to bankruptcy. We conduct a comparative analysis based on both Canonical Discriminant Analysis and Logit models to examine and to determine the most accurate one....

Economically intuitive macroeconomic factors and borrower characteristics predict peer-to-peer loan defaults beyond what proprietary algorithms predict. Using county-level unemployment data, we find that loans originated in high unemployment areas are more likely to default. In addition, we...

In this paper, we compare the performance of two non-parametric methods of classification, Regression Trees (CART) and the newly Multivariate Adaptive Regression Splines (MARS) models, in forecasting bankruptcy. Models are implemented on a large universe of US banks over a complete market cycle...

This study aims to evaluate the techniques used for the validation of default probability (DP) models. By generating simulated stress data, we build ideal conditions to assess the adequacy of the metrics in different stress scenarios. In addition, we empirically analyze the evaluation metrics...

This paper devises and tests a state-dependent approach to forecasting the downside risk of financial assets. The approach has three merits. First, it proposes downside risk prediction conditional on the state of the real economy to recognize the countercyclical nature of financial risk. Second,...