۱- Introduction

۲- Determinants of credit spreads

۳- Data

۴- Methodological framework

۵- Empirical results

۶- Out-of-sample robustness tests

۷- Summary and conclusion

References

Abstract

This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing.

Introduction

The aim of our study is to identify the macroeconomic and financial factors and model the forces that drive credit spreads on bond indices in the US credit market. Much of the research on corporate credit risk focuses on the credit spreads, which is a common measure of a company’s borrowing cost and creditworthiness. The theoretical work on credit spreads, defined as the difference between the interest rate the corporate borrower pays on its debt and the rate paid by the Treasury on debt of a comparable maturity, suggests that they change over time for reasons such as varying market conditions, changes in issuer credit ratings, or changes in expectations regarding the recovery rate (see Campbell and Huisman 2003; Longstaff and Schwartz 1995, among others). The empirical literature (e.g. Delianedis and Geske 2001; Collin-Dufresne et al. 2001; Elton et al. 2001; Cremers et al. 2008; Gilchrist and Zakrajsek 2011; Huang and Huang 2012) suggests that besides the theoretical factors related to default risk, there is also a set of macro-economic factors that explain a proportion of the variation in credit spreads. This literature generally uses standard econometric techniques that do not address the particularities of the credit spread data, such as time varying volatility, skewness and thick tails, which could bias the results. However, empirical evidence shows that credit spreads are likely to have time varying, non-normal distributions. For example, Pedrosa and Roll (1998), Cai and Jiang (2008) and Hibbert et al. (2011) showed that US corporate credit spread indexes reveal a high level of persistence in volatility and in a study of the Euro zone fixed income markets Alizadeh and Gabrielsen (2013) showed that credit spread changes are likely to be skewed, fat-tailed, and change behaviour over time.1 Ignoring characteristics such as these can bias the results and compromise the estimation of credit spread models. This paper addresses this shortcoming by proposing asymmetric GARCH models with alternative probability density functions to identify the variables that drive changes in credit spreads.