Market-Based Credit Ratings

Abstract

We present a methodology for rating in real-time 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 firms whose assets are traded to be directly rated by market participants. For firms whose assets are not traded, we show how they can be indirectly rated by matching them to firms that are traded based on observable characteristics. We also show how the resulting ratings can be used to construct loss distributions for portfolios of bonds. Finally, we compare our ratings to Standard & Poors and find that, over the period 2005 to 2011, our ratings lead theirs for firms that ultimately default.

KEY WORDS:

• Clustering
• Credit default swaps
• Default risk
• Survival functions

Notes

We acknowledge that firms may look identical based on observable characteristics but differ based on firm-specific unobservable characteristics. Assessing the credit risk of firms that have no assets traded and which differ from traded firms based on unique unobservable characteristics is a difficult task because the ratings must be determined solely by prior information.

In Section 3, we map the raw credit default swap data into survival functions using an asset pricing model that imposes no-arbitrage and consequently a declining survival function. Therefore, the survival functions automatically satisfy this constraint.

This type of bootstrap should not be confused with the bootstrap used in statistics.

The grades are mapped into numbers as AAA → 25, AA+ → 24, AA → 23, AA− → 22, A+ → 21, A → 20, A− → 19 BBB+ → 18, BBB → 17, BBB− → 16, BB+ → 15, BB → 14, BB− → 13, B+ → 12, B → 11, B− → 10, CCC+ → 9, CCC → 8, CCC− → 7, CC+ → 6, CC → 5, CC− → 4, C+ → 3, C → 2, C → 1, Default → 0, Not Rated → −1.