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

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