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    ABSTRACT: We estimate the probability of delinquency and default for a sample of credit card loans using intensity models, via semi-parametric multiplicative hazard models with time-varying covariates. It is the first time these models, previously applied for the estimation of rating transitions, are used on retail loans. Four states are defined in this non-homogenous Markov chain: up-to-date, one month in arrears, two months in arrears, and default; where transitions between states are affected by individual characteristics of the debtor at application and their repayment behaviour since. These intensity estimations allow for insights into the factors that affect movements towards (and recovery from) delinquency, and into default (or not). Results indicate that different types of debtors behave differently while in different states. The probabilities estimated for each type of transition are then used to make out-of-sample predictions over a specified period of time.
    No preview · Article · Jul 2014 · European Journal of Operational Research
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    Full-text · Article · May 2013 · Environmental Science & Technology
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    ABSTRACT: This paper describes and analyses a Stochastic Programming (SP) model that is used for a specific inventory control problem for a per-ishable product. The decision maker is confronted with a non-stationary random demand for a fixed shelf life product and wants to make an or-dering plan for a finite horizon that satisfies a service level constraint. In literature several approaches have been described to generate approxi-mate solutions. The question dealt with here is whether exact approaches can be developed that generate solutions up to a guaranteed accuracy. Specifically, we look into the implications of a Stochastic Dynamic Pro-gramming (SDP) approach.
    Full-text · Article · Jan 2012
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