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

Copula-Based Factor Model for Credit Risk Analysis