Analyzing credit spread changes using explainable artificial intelligence
比较线性回归、局部多项式回归和机器学习方法对信用利差变化的建模效果，利用部分依赖图和SHAP值分解影响因素，发现机器学习优势主要来自非线性而非交互作用，并应用于美国和欧元区公司债及担保债券。
We compare linear regression, local polynomial regression and selected machine learning methods for modeling credit spread changes. Using partial dependence plots (PDPs) and H-statistic, we find that the outperformance of machine learning models compared to regression ones is mostly attributable to complex non-linearities and not to interactions. The PDPs are additionally used to perform a factor hedging. For the first time, credit spread changes are decomposed by applying SHapley Additive exPlanation (SHAP) values. The proposed framework is applied to US and Euro Area corporate and covered bond credit spread changes of different maturities to quantify the influence of several macroeconomic and financial variables. Despite several commonalities between the decompositions of US and Euro Area credit spread changes, we also observe some differences - particularly related to the impact of certain explanatory variables during crisis periods.