Credit Risk Measurement
Qualitative measures and disclosures that help firms to mitigate credit risk have long been discussed in extant literature but rapid progress in the development of many sophisticated models to quantitatively measure credit risk have been witnessed in recent studies. Modeling credit risk has always been essential to predict bankruptcy or financial distress of firms over the time. The approaches to evaluate credit risk vary because each approach relies on different determinants of credit risk. For instance, some approaches consider financial fundamentals while others take into account market prices or spreads to gauge credit risk. Nevertheless, these approaches have contributed in the development of quantitative methods of credit risk measurement. Two classical quantitative methods to assess credit risk are Multiple Discriminant Analysis (MDA) and Conditional Probability Analysis (CPA). Different researchers have developed several empirical models based upon these two popular methods. The model designed by Edward I. Altman in 1968 inspired by MDA and CPA based model developed by James A. Ohlson in 1980 have widely been used in relevant literature.
- Distinguish between Multiple Discriminant Analysis (MDA) and Conditional Probability Analysis (CPA) by identifying statistical and theoretical differences in these methods.
- You are required to find out and briefly discuss the empirical models developed by Altman in 1968 and Ohlson in 1980 to gauge credit risk. Also list down and elaborate the independent variables used in each empirical model.
- How the models (discussed in part 2) vary in measuring credit risk?