The Empirical Determinants of Credit Default Swap Spreads: a Quantile Regression Approach

Abstract

We study the empirical determinants of Credit Default Swap (CDS) spreads through quantile regressions. In addition to traditional variables, such as implied volatility, put skew, historical stock return, leverage, profitability, and ratings, the results indicate that CDS premiums are strongly determined by CDS illiquidity costs, measured by absolute bid-ask spreads. The quantile regression approach reveals that high-risk firms are more sensitive to changes in the explanatory variables that low-risk firms. Furthermore, the goodness-of-fit of the model increases with CDS premiums, which is consistent with the credit spread puzzle.

The Drivers of Sovereign CDS Spread Changes: Local Versus Global Factors

“…Although we empirically investigate the drivers of daily sovereign CDS spread changes across the conditional distribution, further research can look into how the dependence of spreads on other financial variables can be used for forecasting. Studying the distribution is critical for estimating Value‐at‐Risk (VaR), and further research could continue in the area of forecasting VaR and co‐VaR along the lines of studies by Pires, Pereira and Martins () and Adrian and Brunnermeier ().…”

“…We contribute to the sovereign CDS literature by exploring the heterogeneity of the response of sovereign spreads to explanatory factors by using a quantile panel regression with fixed effects approach, which describes the conditional distribution of the dependent variable, as opposed to focusing just on the mean. Our approach is motivated in part by Pires, Pereira and Martins (), who show that ordinary least squares (OLS) mean regression of the determinants of spreads are not precise in describing the center of the CDS spreads distribution and represent to a higher extent the results for the upper quantiles. The quantile panel regression methodology we use follows Koenker () and provides a more flexible and robust approach by consolidating quantile regression with panel data modeling.…”

The Determinants of CDS Spreads: Evidence from the Model Space

“…These studies find evidence that CDS spreads predict negative rating events. More recently, Pires et al (2015) use a quantile regression approach to study the determinants of CDS spreads. The authors find evidence that implied volatility, historical stock returns, leverage, profitability but also illiquidity costs determine CDS premiums.…”

Explaining co-movements between equity and CDS bid-ask spreads

“…Since equity bid and ask prices are quoted in dollar terms, while CDS bid and ask prices are quoted in basis points, for CDS bid-ask spread I use the difference between quoted bid and ask prices (as in Bongaerts et al 2011, Völz and Wedow 2011, Coro et al 2013Pires et al 2015), while for equity bid-ask spread I use the ratio between quoted bid-ask spread and mid-quote price. In the existing literature, the CDS bid-ask spread has been measured by the difference between ask and bid quotes (absolute bid-ask spread, as in Coro et al 2013, andPires et al 2015), or by this difference normalized by the mid-quote point (percentage bid-ask spread, as for example in Hilscher et al 2015).…”

“…In the existing literature, the CDS bid-ask spread has been measured by the difference between ask and bid quotes (absolute bid-ask spread, as in Coro et al 2013, andPires et al 2015), or by this difference normalized by the mid-quote point (percentage bid-ask spread, as for example in Hilscher et al 2015). I favour the former measurement: Pires et al (2015) provide a convincing numerical argument and show that since the CDS bid-ask spread is already a proportional measure there is no need to divide it by the mid-quote (as it is done instead for the equity bid-ask spread). This choice is particularly appropriate to perform a correct comparison between CDS and equity bid-ask 9 At the time of my data collection, CMA data could be freely downloaded from Bloomberg.…”