Copula-Based Factor Model for Credit Risk Analysis

TL;DR

This paper enhances credit risk modeling by integrating a state-dependent recovery rate into a copula-based factor model, improving default forecasts and aligning with Basel III insights on systematic risk influence.

Contribution

It introduces a novel copula-based factor model with shared common factors for default and recovery rates, improving predictive accuracy over existing models.

Findings

The model with random factor loading and state-dependent recovery rate performs best.

Default tendency is more influenced by systematic risk during turbulent periods.

The approach aligns with Basel III's emphasis on systematic risk importance.

Abstract

A standard quantitative method to access credit risk employs a factor model based on joint multivariate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate into the conditional factor loading, and model them by sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously and creates their association implicitly. In accordance with Basel III, this paper shows that the tendency of default is more governed by systematic risk rather than idiosyncratic risk during a hectic period. Among the models considered, the one with random factor loading and a state-dependent recovery rate turns out to be the most superior on the default prediction.