A New Test of Statistical Arbitraje with Applications to Credit Derivatives Markets

This paper presents a new statistical arbitrage test which has lower Type I error and selects arbitrage opportunities with lower downside risk than existing alternatives. The test is applied to credit derivatives markets using strategies combining Credit Default Swaps and Asset Swaps. Using four different databases (GFI, Reuters, CMA and JP Morgan) from 2005 to 2009, we find persistent mispricings before and dur-ing the current financial crisis. However, after considering funding and trading costs, these mispricings are unlikely to provide profitable arbitrage opportunities