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Pricing Credit Default Swaps with Option-Implied Volatility

Using the industry benchmark CreditGrades model to analyze credit default swap (CDS) spreads across a large number of companies during the 2007–09 credit crisis, the authors demonstrate that the performance of the model can be significantly improved by calibrating it with option-implied volatility rather than with historical volatility. Moreover, the advantage of using option-implied volatility is greater among companies with more volatile CDS spreads, more actively traded options, and lower credit ratings. Structural credit risk models are important tools in relative value trading strategies because they use equity market information to price such credit-risky securities as corporate bonds and credit default swaps (CDSs). One of the most important inputs for such models is equity volatility, which can be estimated from either historical returns or equity option prices. We examined the relative performance of historical versus option-implied volatility in CDS pricing through the lens of an industry benchmark model called CreditGrades, which was jointly developed by the RiskMetrics Group, J.P. Morgan, Goldman Sachs, and Deutsche Bank in 2002. Using CDS and options market data on 332 companies over January 2007–October 2009 (which encompasses the recent credit crisis), we found that option-implied volatility generally dominates historical volatility in both in-sample and out-of-sample pricing performance. This finding is robust to the horizon of the historical volatility estimator and the initial burn-in period for calibrating the CreditGrades model. It also remains qualitatively unchanged during the earlier and less chaotic sample period of 2001–2004. Perhaps more interestingly, we identified significant cross-sectional variations in such relative performance. For instance, option-implied volatility provides much more added value in terms of improved CDS pricing performance when the company in question has a lower credit rating, a more volatile CDS spread, and larger options-trading volume. Our interpretation is that these characteristics are associated with a higher signal-to-noise ratio of options market information. Our findings are important to market participants who need to monitor their credit risk exposures constantly. The improvement in pricing performance is likely to result in fewer false trading signals and superior profitability for capital structure arbitrageurs. On a more fundamental level, our findings suggest that having forward-looking inputs from the market could be as important as having the right model for pricing credit risk.