Challenges, Methodologies, and the Impact of Macroeconomic Factors on Credit Risk Management

Article

Adapting credit risk models to changing economic environments requires diverse methodologies tailored to specific quantitative problems. Logistic regression is widely used, but methodologies vary based on the objective, such as modeling short-term or long-term probabilities of default (PD). For long-term PD models, incorporating macroeconomic variables through approaches like vector autoregressive models is essential.

Aug 06, 2024

Varun Nakra, VP Credit Risk Modelling, Deutsche Bank

Challenges, Methodologies, and the Impact of Macroeconomic Factors on Credit Risk Management

The choice of methodologies for credit risk models depends on the specific quantitative problem, with logistic regression being a common technique.
Short-term probability of default (PD) models use regression and scorecards, but converting these to long-term models requires different methods, such as vector autoregressive approaches incorporating macroeconomic variables.
Models must be redeveloped or recalibrated due to data changes, regulatory updates, and advancements in techniques.
Stress testing assesses resilience under various macroeconomic scenarios, using both traditional and modern AI methods.
Challenges include ensuring model interpretability and managing unstructured data. Advanced models offer improved accuracy but can lack intuitive understanding.