Paper: Credit Risk Clustering with Covariate-dependent Copula Models

with Zhuojing He from Business School, Central University of Finance and Economics.

Abstract

Understanding how corporate defaults cluster is particularly important for risk management of portfolios of corporate debt. In this paper, we discuss the dynamic nature of the clustering of credit risk across firms pairwise in the same family corporation in China. We insert the tail-dependence coefficient into the Joe-Clayton copula model directly through a reparameterized methodology to estimate the tail-dependence structure of credit risk. We also use both macroeconomic and firm-specific covariates to study the dynamic nature of the lower tail-dependence coefficient of distance-to-default which measures the credit risk clustering, and to find the driving forces behind credit risk clustering. Empirical results indicate that both macroeconomic and firm-specific covariates play important roles in the time-varying features of credit risk clustering. However, for different pairwise portfolios, these macroeconomic and firm-specific covariates have different effects.

Keywords: Credit risk clustering; Covariate-dependent copulas; tail-dependence; distance-to-default; MCMC.

Supplementary Material

In our study we apply our method to total of 45 pairwise firms. There are 39 pairs showing significant results, three of which are already explained in the paper. The supplementary material shows the empirical results of credit risk clustering across 36 significant pairwise firms not listed in the paper.

Published
Categorized as Paper

By Feng Li

Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. Feng obtained his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. His research interests include Bayesian computation, econometrics and forecasting, and distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.

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