Mr.Jorge A. Chan-Lau

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 well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.

Lasso Regressions and Forecasting Models in Applied Stress Testing

Jochen M. Schmittmann

MissEstelle X. Liu

This study finds that equity returns in the banking sector in the wake of the Great Recession and the European sovereign debt crisis have been driven mainly by weak growth prospects and heightened sovereign risk and to a lesser extent, by deteriorating funding conditions and investor sentiment. While the equity return performance in the banking sector has been dismal in general, better capitalized and less leveraged banks have outperformed their peers, a finding that supports policymakers’ efforts to strengthen bank capitalization.

Mr.Jorge A. Chan-Lau

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