Common factors in credit defaults swap markets

Abstract

We examine what are the common factors that determine systematic credit risk, and estimate and interpret these factors. We also compare the contributions of common factors in explaining the changes of credit default swap spreads during the pre-crisis, the crisis and the post-crisis period; there is evidence to suggest that the eigenstructures across these three sub-periods are distinct. Furthermore, we examine whether the observable economic variables are in fact the underlying latent factors and analyze the predictability in the factors that capture the time-variation of credit default swap spreads.

Splitting Credit Risk into Systemic, Sectorial and Idiosyncratic Components

“…The maintained high commonality of risk after May 2008 could have been taken at that time, well in advance of the Lehman crisis, as an indication of potential future problems. These results are comparable to those obtained by Berndt and Obreja (2010) and Chen and Härdle (2015), among others.…”

“…As shown in Table 7, the first principal component explains 65% of the fluctuations in the weekly changes of the 11 sectorial indices, a confirmation that there is strong commonality among the sectors. This is higher than the one estimated by Berndt and Obreja (2010) for European firms during the 2003 to 2008 period, but it is close to the average explanatory power estimated by Chen and Härdle (2015) for the pre-(58.7%) and post-crisis periods (72.3%). Note: The table shows the eigenvalues of the matrix of standardized weekly changes in the sectorial credit indices, the cumulative variance explained by the eigenvectors, and the coefficients of each of the first four eigenvectors on the 11 sectorial indices.…”

“…Using a sample of 150 European firms from January 2003 to July 2007, Berndt and Obreja (2010) show that the first principal component of CDS returns explained 46% of the variation in weekly CDS returns, even after correcting for a market factor (weekly excess return on the Morgan Stanley Capital International Europe (MSCI) index) Europe index) and a term premium, computed as the spread between the weekly return on the ten-year Euribor bond and the one-week Euribor. Chen and Härdle (2015) found that the first principal component for a set of eight iTraxx and CDX indices of 5-and 10-year maturities and investment grade and high-yield ratings explained 58.7% of the variance in the pre-crisis period, increasing up to 72.3% of the variance in the crisis period, but only 47% in the post-crisis period. They also concluded that a four-factor model could provide a good fit to weekly changes in CDS indices, with all factors receiving a significant market price.…”

“…In terms of the determinants of risk, Berndt and Obreja (2010) and Chen and Härdle (2015) try to characterize the most influential financial variables that explain credit spread movements by analyzing the impact of some financial variables on individual CDS spreads and CDS indices, respectively. Much of this literature has focused on individual firm data, using accounting data and firm's characteristics as indicators of credit risk.…”

Dynamic Credit Default Swaps Curves in a Network Topology

“…(4) The change in the credit spread between Moody's Baa-rated bonds and the ten-year Treasury rate; (5) The daily market return computed from the S&P500; (6) The daily real estate sector return in excess of the market financial sector return; (7) VIX; In addition we employ common principal components (CPC) the average variance explained by the first principle component through the common principle component approach (CPCA), see (Flury, 1984;Fengler et al, 2003;Chen and Härdle, 2015). The CPC factor here is used to capture a common factor which may not be directly observed.…”