Year: 2019
Type: dissertation
Abstract: This thesis consists of four chapters, all of which are related to credit risk and particularly\nmodeling of default risk. The chapters can be read independently, and the intended audience\ndiffers somewhat among them.\nThe first chapter is methodical; the intended audience consists of statisticians and practitioners\nwho are end users of the software described in the chapter. In particular, the first chapter\nis written for biostatisticians, statisticians, or practitioners with some prior experience with survival\nanalysis. The chapter shows fast approximate methods to estimate a class hazard models\nimplemented in an open source R package.\nThe second chapter focuses on default risk models for a broad group of public and private firms.\nThese models are particularly interesting for regulators and banks that wants to evaluate the risk\nof a corporate debt portfolio with varying exposure. The intended audience consists of academics,\nparticularly those working within finance with default models, as well as practitioners, either on\nthe regulatory or private side. The main question of the chapter is whether the typically observed\nexcess clustering of defaults is due to a misspecification of the dependence between observable\nvariables and the probability of entering into default. While we do find improvements on the firmlevel\nafter relaxing standard assumptions, the improvements are substantially smaller than stated\npreviously in the literature. Moreover, we find limited evidence that the more general models fit\nbetter on an aggregate scale. Thus, we show an easily implemented random effect model that\ninvolves similar relaxations, achieves comparable firm-level performance, and performs better on\nthe aggregate scale.
Source: Econstor (Econstor)
Author: Benjamin Christoffersen
Institution: Copenhagen Business School
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3008692164