Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2015 042

Copula-Based Factor Model for Credit Risk Analysis

Lu, Meng-Jou
Chen, Cathy Yi-Hsuan
Härdle, Karl Wolfgang

A standard quantitative method to access credit risk employs a factor model based on joint
multi-variate 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.

Factor Model, Conditional Factor Loading, State-Dependent Recovery Rate

JEL Classification:
C38, C53, F34, G11, G17