Whilst they were almost entirely absent from the economics and finance literature, many industry articles discuss factor-based approaches, including incidence and severity models. Luckner and Young (1999) provide a case study that looks at credit risk using the incidence-and-severity model, and explicitly defines economic loss for a "credit risk event" as a ratio of the difference between the present value of cash flows with and without credit risk events divided by the present value of cash flows without credit risk events. Agency ratings also receive a great deal of focus, from simple Markov transition matrices to ordinal logistic regression and stochastic analysis. Hambro and Houghton (2001) provide a general introduction to interest rate and credit risk—deterministic and stochastic simulation, spreads, and the various types of credit-related risk, and deterministic modeling of credit risk using incidence-and-severity. Houghton also presents a stochastic modeling approach to credit risk that includes both asset default and downgrades using transition matrices. Bae and Kulpergry (2008) propose a multiperiod ordinal logistic regression model for credit rating transition probabilities. They extend single period factor based models to a multiperiod case. Han (2008) provides a historical perspective to both single-factor and multifactor credit models. He then demonstrates the use of one credit model to optimize a credit portfolio using multiple credit transition metrics.