Jan Ericsson

The Determinants of Credit Default Swap Premia

Variables that in theory determine credit spreads have limited explanatory power in existing empirical work on corporate bond data. We investigate the linear relationship between theoretical determinants of default risk and default swap spreads. We find that estimated coefficients for a minimal set of theoretical determinants of default risk are consistent with theory and are significant statistically and economically. Volatility and leverage have substantial explanatory power in univariate and multivariate regressions. A principal component analysis of residuals and spreads indicates limited evidence for a residual common factor, confirming that the theoretical variables explain a significant amount of the variation in the data.

Pricing Credit Default Swaps with Observable Covariates

Observable covariates are useful for predicting default, but several studies question their value for explaining credit spreads. We introduce a discrete-time no-arbitrage model with observable covariates, which allows for a closed-form solution for the value of credit default swaps (CDS). The default intensity is a quadratic function of the covariates, specified such that it is always positive. The model yields economically plausible results in terms of fit, the economic impact of the covariates, and the prices of risk. Risk premiums are large and account for a smaller percentage of spreads for firms with lower credit quality. Macroeconomic and firm-specific information can explain most of the variation in CDS spreads over time and across firms, even with a parsimonious specification. These findings resolve the existing disconnect in the literature regarding the value of observable covariates for credit risk pricing and default prediction. The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

An Empirical Study of Structural Credit Risk Models Using Stock and Bond Prices

Reduced-form credit risk models are often thought to be better suited than structural models for pricing corporate bonds. The authors challenge this view. Conditioned not only on equity but on bond and dividend information also, a structural model performs well compared to previously tested reduced-form models. In the pricing of bond portfolios, model errors are to a large extent diversifiable.

Exploring Dynamic Default Dependence

Characterizing the dependence between companies’ defaults is a central problem in the credit risk literature, and the dependence structure is a first order determinant of the value of credit portfolios and structured credit products such as collateralized debt obligations (CDO), as well as the relative values of CDO tranches. We compare correlation measures implied by CDO prices with time-varying correlations implied by equity returns and CDS spreads. We use flexible dynamic equicorrelation techniques introduced by Engle and Kelly (2008) to capture time variation in CDS-implied and equity return-implied correlations. We perform this analysis using North American firms from the CDX index, as well as European firms from the iTraxx index. All correlation time series are highly time-varying and persistent, and correlations extracted from CDSs and CDOs increased significantly in European and North American markets during the turbulent second half of 2007. Interestingly, we find that the correlation time-series implied by CDO prices co-moves very strongly with the correlation time-series extracted from CDS spreads, but somewhat less strongly with the correlations between equity returns. These findings suggest that the cross-sectional dependence in these complex structured products is fairly well measured. However, changes in CDO prices may be due to changes in correlation, and more sophisticated models with time-varying correlations are thus needed to value CDOs.