Beating Credit Benchmarks

Terry Benzschawel

This article was first published as a chapter in Credit Modelling (2nd edition), by Risk Books.

The performances of portfolio managers and other investment professionals are often assessed relative to pre-specified benchmark portfolios. Since the early 2000s, we have been exploring ways to help investors beat return targets using model-based measures of credit risk and relative value. In this chapter, we will describe a procedure, called the cut-and-rotate (C&R) method, which is based on estimates of PDs and relative value obtained from structural models of credit risk.¹¹We used Moody’s KMV model, a commercially available product, for our original studies (Crosby, 1999; Kealhofer and Kurbat, 2001) and later acquired Sobehart and Keenan’s (2002, 2003) HPD model. Both models use information from the equity market, along with balance sheet and income statement information, to assess the default risk of public firms. The C&R procedure has evolved from an initial study of credit picking “robots” in 2003. That is, several different automated credit selection algorithms were used to construct portfolios using model-based PDs and yields from bonds in investment-grade and high-yield credit