A machine learning approach in stress testing US bank holding companies
研究了机器学习结合宏观和微观数据在压力测试中提升风险分析的效果，发现随机森林和自适应Lasso模型优于传统线性模型，能更准确预测银行关键变量并揭示系统性风险。
This paper assesses the utility of machine learning (ML) techniques combined with comprehensive macroeconomic and microeconomic datasets in enhancing risk analysis during stress tests. The analysis unfolds in two stages. I initially benchmark ML’s efficacy in forecasting two pivotal banking variables, net charge-off (NCO) and pre-provision net revenue (PPNR), against traditional linear models. Results underscore the superiority of Random Forest and Adaptive Lasso models in this context. Subsequently, I use these models to project PPNR and NCO for selected bank holding companies under adverse stress scenarios. This exercise feeds into the Tier 1 common equity capital (T1CR) densities simulation. T1CR is the equity capital ratio corrected by some regulatory adjustments to risk-weighted assets. Crucially, findings reveal a pronounced left skew in the T1CR distribution for globally systemically important banks vis-à-vis linear models. By mirroring distress akin to the Great Recession, ML models elucidate intricate macro-financial linkages and enhance risk assessment in downturns. • ML modeling improves forecast accuracy of PPNR and NCO over linear models. • ML modeling better approximates T1CR density than linear models in turmoil. • Linear models tend to underestimate systemic risk in stress test. • ML modeling provides realistic picture of complex macro financial linkages.