ARE WATCH PROCEDURES A CRITICAL INFORMATIONAL EVENT IN THE CREDIT RATINGS PROCESS? AN EMPIRICAL INVESTIGATION

The Boot, Milbourn, and Schmeits (2006) model (Boot model) predicts certain credit rating events are likely to be more informative than others and that credit watch procedures are an important driver of such differences. We test the core empirical predictions of their model. Our sample comprises U.S. corporate issuer credit ratings provided by Moody's, 1990–2006. Our findings fail to uncover compelling evidence for the empirical predictions of the Boot model in relation to the role of watch procedures as coordinating mechanisms. Rather, our findings are more supportive of the view that rating agencies are always at an informational advantage relative to investors.     

Das Gesetz von Gauss und die Theorie des Risikos

Abstract

1. Die Theorie des Risikos in der Lebensversicherung hat für die Praxis bisher wohl nie die Bedeutung erlangt, die man von verschiedenen Seiten für sie beansprucht hat. Die Beurteilung der Stabilität einer Versicherungsanstalt gegen Sterblichkeitsschwankungen, die Festsetznng von Höchstbeträgen auf eigenem Risiko, die Bildung und Verwaltung der Sicherheitsreserven — alles dies wird wohl im allgemeinen nach verhältnissmässig wenig durchgedachten praktischen Erwägungen geregelt, ohne dass dabei die Theorie zur Sprache kommen darf.     

Response measurement and optimization of direct mailings

Beginning with 1995, we discuss different studies that deal with response measurement and optimization of direct mailings. Most of these studies analyze data sets from mail order companies or charities. We classify various dependent and predictor variables and??w.r.t. the latter distinguish static and dynamic effects. Response models are divided into parametric and flexible models. Besides, we analyze important modeling aspects, i.e., latent heterogeneity and endogeneity. Optimization methods are presented according to whether they refer to static or dynamic objectives. Based on these modeling aspects we evaluate the different studies. Considering various studies of model evaluation it becomes evident that logit models frequently constitute a good choice. However, Bayesian neural nets and Tobit models turn out to be good alternatives. As predictor effects are concerned results vary. Authors do not completely agree on which variables are the most important. Furthermore, signs and significances of predictors vary across studies. The majority of studies neglect latent heterogeneity and endogeneity. Finally, results show that there are still plenty of interesting research possibilities, such as a comprehensive evaluation of models or new specifications of (mailing) variables.