Analyzing credit spread changes using explainable artificial intelligence

Julia Heger, Aleksey Min and Rudi Zagst

International Review of Financial Analysis, 2024, vol. 94, issue C

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C10 C18 C45 C52 C53 C58 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1057521924002473
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002473

DOI: 10.1016/j.irfa.2024.103315

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().