Corporate Default Models

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Corporate Default Models

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dc.contributor.author Christoffersen, Benjamin
dc.date.accessioned 2019-12-16T09:31:50Z
dc.date.available 2019-12-16T09:31:50Z
dc.date.issued 2019-12-16
dc.identifier.isbn 9788793956063
dc.identifier.isbn 9788793956070
dc.identifier.issn 0906-6934
dc.identifier.uri http://hdl.handle.net/10398/9773
dc.description.abstract This thesis consists of four chapters, all of which are related to credit risk and particularly modeling of default risk. The chapters can be read independently, and the intended audience differs somewhat among them. The first chapter is methodical; the intended audience consists of statisticians and practitioners who are end users of the software described in the chapter. In particular, the first chapter is written for biostatisticians, statisticians, or practitioners with some prior experience with survival analysis. The chapter shows fast approximate methods to estimate a class hazard models implemented in an open source R package. The second chapter focuses on default risk models for a broad group of public and private firms. These models are particularly interesting for regulators and banks that wants to evaluate the risk of a corporate debt portfolio with varying exposure. The intended audience consists of academics, particularly those working within finance with default models, as well as practitioners, either on the regulatory or private side. The main question of the chapter is whether the typically observed excess clustering of defaults is due to a misspecification of the dependence between observable variables and the probability of entering into default. While we do find improvements on the firmlevel after relaxing standard assumptions, the improvements are substantially smaller than stated previously in the literature. Moreover, we find limited evidence that the more general models fit better on an aggregate scale. Thus, we show an easily implemented random effect model that involves similar relaxations, achieves comparable firm-level performance, and performs better on the aggregate scale. en_US
dc.format.extent 129 en_US
dc.language eng en_US
dc.relation.ispartofseries PhD Series;32.2019
dc.title Corporate Default Models en_US
dc.type phd en_US
dc.contributor.corporation Copenhagen Business School. CBS en_US
dc.contributor.department Institut for Finansiering en_US
dc.contributor.departmentshort FI en_US
dc.contributor.departmentuk Department of Finance en_US
dc.contributor.departmentukshort FI en_US
dc.publisher.city Frederiksberg en_US
dc.publisher.year 2019 en_US
dc.title.subtitle Empirical Evidence and Methodological Contributions en_US


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