This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account-specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-specific, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions. Two model-identification methods are examined, based on maximum-likelihood estimation and the generalized method of moments. The latter allows for an operational-statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business-relevant loss functions. The DCS framework is applied to a large set of account-level repayment data. The improvements in classification and prediction performance compared to standard bank-internal scoring methods are found to be significant. This paper was accepted by Noah Gans, stochastic models and simulation.

Abstract This paper analyzes the loss allocation to first, second, and third loss positions in European collateralized debt obligation transactions. The quality of the underlying asset pool plays a predominant role for the loss allocation. A lower asset pool quality induces the originator to take a higher first loss position, but, in a synthetic transaction, a smaller third loss position. The share of expected default losses, borne by the first loss position, is largely independent of asset pool quality but lower in securitizations of corporate loans than in those of corporate bonds. Originators with a good rating and low Tobin’s Q prefer synthetic transactions.

Our main goal is to investigate the question of which interest‐rate options valuation models are better suited to support the management of interest‐rate risk. We use the German market to test seven spot‐rate and forward‐rate models with one and two factors for interest‐rate warrants for the period from 1990 to 1993. We identify a one‐factor forward‐rate model and two spot‐rate models with two factors that are not significantly outperformed by any of the other four models. Further rankings are possible if additional criteria are applied.

Based on a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts in the form of a self-exciting point process, a bank can control the arrival intensity of repayments using costly account-treatment actions. A semi-analytic solution to the corresponding stochastic optimal control problem is obtained using a recursive approach. For a linear cost of treatment effort, the optimal policy in the two-dimensional (intensity, balance) space is described by the frontier of a convex action region. The unique optimal policy significantly reduces a bank’s loss given default and concentrates the collection effort onto the best possible actions at the best possible times so as to minimize the sum of the expected discounted outstanding balance and the discounted cost of the collection effort, thus maximizing the net value of any given delinquent credit-card account. This paper was accepted by Noah Gans, stochastic models and simulation.