CreditGrades and the iTraxx CDS Index Market

In the study reported, the CreditGrades model was used to calculate credit default swap spreads and the spreads were compared with empirically observed CDS spreads for eight iTraxx indices covering Europe. Theoretical and empirical spread changes were found to be significantly correlated. Also, lagged theoretical spread changes were correlated with current iTraxx spread changes. The correlations indicate a close relationship between the stock market and the CDS market and also indicate some predictive ability of the CreditGrades model. Simple trading strategies based on the autocorrelation and predictive ability of the model produced positive profits, before trading costs, when trading was within the bid–ask spread.Credit default swaps are credit derivatives that protect the buyer against losses arising from some kind of predefined credit event (delayed payment, restructuring, bankruptcy, etc.) involving a reference name (company). The price of a credit default swap depends on the probability of the underlying reference name experiencing a credit event in the future. For companies that are listed on stock exchanges, this probability is often estimated from information contained in the company’s stock price. In this study, I thus calculated the theoretical CDS spreads through a simple implementation of the stock market–based CreditGrades (CG) model.A CDS index is a portfolio of credit default swaps. One of the major families of CDS indices is the iTraxx CDS indices. In this article, I compared empirically observed CDS prices (spreads) with the CG-implied CDS prices (spreads) in the iTraxx market. I compared the spreads for eight iTraxx indices covering Europe—seven investment-grade indices and a subinvestment-grade index—with the CG-implied CDS spreads for June 2004 to March 2006.I found theoretical and empirical spread changes to be significantly correlated. I also found one-day-lagged theoretical spread changes to be correlated with current iTraxx spread changes. The significant correlations were confirmed by significant ordinary least-squares (OLS) regression parameters and indicate a fairly close relationship between the stock market and the CDS market, as well as a certain predictive ability of the CG model.Although I found no autocorrelation among the theoretical CG spread changes, I did find significant autocorrelation in the iTraxx market. One-day-lagged theoretical spread changes were also found to be significantly correlated with current iTraxx spread changes. The correlations were confirmed by significant OLS regression parameters and indicate a fairly close relationship between the stock market and the CDS market. This finding also suggests a certain predictive ability of the CG model.Based on these findings, I show how simple trading strategies (autoregression, capital structure arbitrage, and a combined strategy) can produce positive profits if the trades are within the bid–ask spread. The introduction of realistic transaction costs, however, significantly reduced the profitability of the trading strategies.Readers should keep in mind, however, that the iTraxx market is young. In the years to come, the growing maturity of the market will most likely lead to a fall in transaction costs (bid–ask spreads). In addition, the period I studied was one with few credit defaults. Both the risks and the rewards are likely to increase with any marketwide credit deterioration. Finally, trading strategies that are more advanced than the simple strategies I illustrate may produce increased after-cost profits.