High-dimensional macroeconomic stress testing of corporate recovery rate
用机器学习方法分析182个宏观经济变量对企业债券回收率的影响，基于美国联邦储备局的压力情景，用最小二乘支持向量回归模型计算风险价值等指标。
We investigate macroeconomic stress testing frameworks for corporate bond recovery rate analysis using machine learning techniques. In doing so, we simulate the macroeconomic effect of a broad range of 182 macroeconomic variables extracting key factors with methods such as (sparse) principal component analysis and sparse group least absolute selection and shrinkage operation (LASSO). Using the adverse stress testing scenario from the US Federal Reserve as the benchmark, we demonstrate that our least squares-support vector regression model produces sensible and potentially valuable risk measures such as value-at-risk and conditional value-at-risk for recovery rates during periods of macroeconomic stress.