Conditional Inferences Based on Vine Copulas with Applications to Credit Spread Data of Corporate Bonds

TL;DR

This paper applies vine copula models with tail dependence to analyze Chinese corporate bond credit spreads, enabling better understanding of sector dependencies and more accurate risk assessments through conditional inferences.

Contribution

It introduces a novel application of vine copula models with tail dependence for credit spread analysis and demonstrates improved predictive accuracy over linear regression.

Findings

Vine copula models reveal sector dependence structures.

Tail dependence significantly impacts risk transfer analysis.

Conditional inferences improve prediction accuracy for credit spreads.

Abstract

Understanding the dependence relationship of credit spreads of corporate bonds is important for risk management. Vine copula models with tail dependence are used to analyze a credit spread dataset of Chinese corporate bonds, understand the dependence among different sectors and perform conditional inferences. It is shown how the effect of tail dependence affects risk transfer, or the conditional distributions given one variable is extreme. Vine copula models also provide more accurate cross prediction results compared with linear regressions. These conditional inference techniques are a statistical contribution for analysis of bond credit spreads of investment portfolios consisting of corporate bonds from various sectors.