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Analyzing credit spread changes using explainable artificial intelligence
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DOI：10.1016/j.irfa.2024.103315.png)
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Abstract
En 中文
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.
Keywords:
Credit spread changes
Random forest
Partial dependence plot
H-statistic
SHAP values
Hedging
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