Investigating Linkages between U.S. CDS Spreads and Both Equity Market Price and Equity Market Volatility Channels: A Quantile Cointegrating Regression Approach

Abstract

We investigate the behavior of daily aggregate U.S. CDS spreads during almost six years. Existing linkages between credit and equity markets advocate the use of market price and volatility channels as explanatory factors. In particular, the evolution of CDS spreads is analyzed along with the level of the equity market and a well-chosen implied volatility index. We describe such robust (to spurious correlation) relationship with the quantile (cointegrating) regression approach. It allows for describing distributional patterns of CDS spreads in the light of the equity market’s influence. Going beyond a simple mean relationship, the quantile regression tool quantifies accurately the linkages between credit and market risks. Such powerful econometric approach handles extreme quantiles (i.e. extreme CDS values) and their behavior with respect to the equity market’s impact. Moreover, heteroskedastic patterns such as time-varying variance, but also autocorrelation, skewness and leptokurtosis are captured. Incidentally, the sensitivity of aggregate CDS spreads to equity market price and volatility channels vary across quantiles. Being quantile-dependent, the sensitivity of credit risk with respect to (equity) market risk exhibits asymmetric responses to equity market shocks. A complementing sub-period analysis investigates potential regime shifts in estimated quantile cointegrating regressions. Quantile cointegrating coefficients vary over time and quantiles, and exhibit different magnitudes across sub-periods. However, the nature of the full sample-specific impact of equity market on CDS spreads is mostly preserved across sub-periods. As a result, the assessment of credit risk with respect to the impact of market risk and the nature of corresponding linkages differ from one quantile to another. Such methodology can help enhance risk management processes, among which value-at-risk (VaR) and expected shortfall. In this light, a scenario analysis and risk signaling application are proposed for credit risk management prospects. Under specific risk levels, credit risky situations are described conditional on the equity market’s impact over time, and related expected aggregate CDS spreads are computed.