Copula-Based Factor Model for Credit Risk Analysis

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.JEL classification: C38, C53, F34, G11, G17

Systemic risk-shifting in U.S. commercial banking

“…Systemic risk, as distinct from default and credit risk faced by banks, has recently received significant attention (Chen et al 2018;Giglio et al 2016;Lu et al 2017;Bordo et al 1998;Alam et al 2015;Hu and Jiang 2018). It arises when there is a breakdown in aggregate financial intermediation that accompanies a financial institution's failure to meet its obligations, and is related to markets questioning the ability of the financial system to absorb losses.…”