Suchergebnisse

Cover -- Contents -- Introduction -- Stress Tests: A Multi-Step Process -- Model Selection Challenges in Stress Tests -- Machine Learning: The Interpretability-Flexibility Tradeoff -- Linear Models: Subset Selection and Shrinkage Methods -- Lasso Applications in Finance, Economics, and Financial Networks -- A Stress Test Application: Forecasting Probabilities of Default -- Conclusions -- References -- Figures -- 1. Geometry of Least Squares, Ridge Regression and Lasso regression -- 2. Lasso and relaxed Lasso, mean squared errors -- Tables -- 1. Lasso and relaxed lasso, coefficient estimates, λ-min specification -- 2. Lasso and relaxed lasso, coefficient estimates, λ-1se specification

Despite increased need for top-down stress tests of financial institutions, performing themis challenging owing to the absence of granular information on banks' trading and loanportfolios. To deal with these data shortcomings, this paper presents a market-basedstructural top-down stress testing methodology that relies in market-based measures of abank's probability of default and structural models of default risk to infer the capital lossesthey could experience in stress scenarios. As an illustration, the methodology is applied toa set of banks in an advanced emerging market economy