Well-performing default predictions show good discrimination and calibration. Discrimination is the ability to separate defaulters from nondefaulters. Calibration is the ability to make unbiased forecasts. I derive novel discrimination and calibration statistics to verify forecasts expressed in terms of probability under dependent observations. The test statistics’ asymptotic distributions can be derived in analytic form. Not accounting for cross correlation can result in the rejection of actually well-performing predictions, as shown in an empirical application. I demonstrate that forecasting errors must be serially uncorrelated. As a consequence, my multiperiod tests are statistically consistent.

I present an analytical valuation framework for the management of fixed-income instruments traded in imperfectly competitive markets, like demand deposits and credit card loans in the banking book, inter alia, to stabilize the abnormal profit margin. Banking book instruments contain embedded options such as withdrawal rights, discretionary pricing, rate clustering and zero-based floors. Analytical solutions speed up computation time to calculate valuations, earnings and risk measures like closed-form expressions for margin spreads, hedge ratios and parameter sensitivities. Asymptotically, according to martingale central limit theorems and thanks to the long-term nature of the banking book, Gaussian approximations can be applied.

Non-maturity deposits like savings accounts or demand deposits contain significant option risks caused by the bank’s discretionary pricing and the customers’ withdrawal right. Option risks follow from inherent non-linear factor exposures. I propose an ordinal response model for deposit rate jumps to identify non-linear factor exposures and a discrete-time term structure model to value the resulting option risks and to derive hedge measures “outside the model”. My delta profile resembles a constant maturity swap, but vega and gamma are more pronounced, which demonstrates that the widespread practice of static hedging with zero bonds is inadequate.

The relation between physical probabilities (rating) and risk-neutral probabilities (pricing) is derived in a large market with a quasi-factor structure. Factor sensitivities and default probabilities are obtainable for all kinds of credits on historical rating data. Since factor prices can be backed out from market data, the model allows the pricing of non-marketable credits and structured products thereof. The model explains various empirical observations: credit spreads of equally rated borrowers differ, spreads are wider than implied by expected losses, and expected returns on CDOs must be greater than their rating matched, single-obligor securities due to the inherent systematic risk.

We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman’s Z -score, and Merton’s distance to default. We reconcile the statistical and structural approaches under a common framework; that is, our approach nests Altman’s and Merton’s approaches as special cases. Empirically, the combined approach is indeed the most powerful predictor, and the numbers of observed defaults align well with the estimated probabilities. With a new transformation method, we obtain cycle-adjusted forecasts that still outperform ratings.

We study the economic benefits from using credit scoring models. We contribute to the literature by relating the discriminatory power of a credit scoring model to the optimal credit decision. Given the receiver operating characteristic (ROC) curve, we derive (a) the profit-maximizing cutoff and (b) the pricing curve. Using these two concepts and a mixture thereof, we study a stylized loan market model with banks differing in the quality of their credit scoring model. Even for small quality differences, the variation in profitability among lenders is large and economically significant. We end our analysis by quantifying the impact on profits when information leaks from a competitor’s scoring model into the market.