Market-based Credit Ratings
by Drew S. Creal of University of Chicago,
September 24, 2012
Abstract: We present a methodology for rating the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public rms whose assets are traded to be directly rated by market participants. For rms whose assets are not traded, we show how they can be indirectly rated through the use of matching estimators. We also show how the resulting ratings can be used to construct loss distributions for portfolios of bonds. Our approach has the advantages of being transparent, computationally tractable, simple to implement, and easy to interpret economically.
Keywords: credit ratings, clustering, credit default swaps, default risk, survival function.