Looking at US firms from 2002 to 2013, the authors determine whether firm-specific data inspired by structural model and market risk factors can explain credit default swap spread changes. They find that the explanatory power of their model is higher for investment-grade firms.
The authors evaluate the ability of firm-specific data inspired by structural model and market risk factors to explain changes in credit default swap (CDS) spreads on a broad dataset of 718 US firms during the period of January 2002–February 2013. The explanatory power of their model is higher for investment-grade firms than for speculative-grade firms, both during and after the global financial crisis.
How Is This Research Useful to Practitioners?
Earlier research on CDS spread changes has used a market model as a proxy for the expected change. The authors focus on CDS spread changes based on the spread’s relationship with market variables as well as firm-specific variables. Their findings highlight that both of these types of variables explain changes in the CDS spread after controlling for each other. However, three explanatory variables (i.e., stock return, volatility of stock return, and median CDS spread) are able to explain CDS spread changes even after controlling for the information embedded in all other explanatory variables. In the absence of these three variables, other variables (e.g., spot rate or term structure of interest rates) are statistically significant and can be used to explain CDS spread changes.
The authors show that ratings explain cross-sectional variation in CDS spreads even after controlling for fundamental variables and that a linear combination of fundamental variables is not effective for information purposes. The authors’ results, however, suggest that the global financial crisis was responsible for a structural shift in the pricing of CDS spreads, especially among investment-grade firms. The ability of ratings to explain CDS spread changes has been severely damaged by the crisis and has diminished to almost zero. The research is useful for all credit finance professionals.
How Did the Authors Conduct This Research?
The dataset is divided into four sets of variables. The authors use a time-series analysis to investigate the ability of various factors to explain CDS spread changes: (1) firm-specific variables, which are stock return, ΔVolatility, and ΔLeverage; (2) common factors, which are the ΔSpot rate, ΔTerm-structure slope, and ΔVIX (Chicago Board Options Exchange Market Volatility Index); (3) Fama and French value (high minus low), size (small minus large), and market factors and a stock liquidity factor (innovations in aggregate liquidity, or IAL); and (4) five Chen, Roll, and Ross (Journal of Business 1986) macro-variables, which are the growth rate of industrial production (MP), unexpected inflation (UI), change in expected inflation (DEI), ΔUTS (term structure), and ΔUPR (risk premium).
Next, individual regressions for each CDS are averaged to estimate coefficients for each CDS. The t-statistics are computed from the cross section of individual regression coefficients. The authors’ focus is on changes in CDS spreads rather than on the levels of CDS spreads because changes are stationary but levels tend not to be.
The authors also conduct a cross-sectional, or level, analysis to examine the ability of ratings and firm-specific variables to explain CDS spreads. First, they examine four different time periods: before the crisis (May 2007), the peak of the crisis (September 2008), a year after the peak (July 2009), and after the crisis (July 2011). Second, to explain CDS prices over time, a cross-sectional regression for each month is averaged to estimate coefficients, separating the results into four time periods: January 2002–February 2013, January 2002–June 2007, July 2007–June 2009, and July 2009–February 2013.
The authors’ inferences and conclusions about determinants of CDS spread changes not only differ from earlier research but also reveal new insight for the literature. They discover that models used in the event study literature to explain spread changes can be improved by using additional market variables.