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Derivatives Read chapter Read first chapter

2019 | OriginalPaper | Chapter

21. Nonparametric Estimators for the Probability of Default

Authors : Jürgen Franke, Wolfgang Karl Härdle, Christian Matthias Hafner

Published in: Statistics of Financial Markets

Publisher: Springer International Publishing

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Abstract

The estimation of the probability of default based on information on the individual customer or the company is an important part of credit screening, i.e. judging the credit standing.

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Literature
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Fahrmeir, L., & Tutz, G. (1994). Multivariate statistical modelling based on generalized linear models. Heidelberg: Springer-Verlag.CrossRef
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Müller, M. (2000). Generalized partial linear models. In W. Härdle, Z. Hlavka, & S. Klinke (Eds.), XploRe application guide. Heidelberg: Springer-Verlag.MATH
Müller, M., & Rönz, B. (2000). Credit scoring using semiparametric methods. In J. Franke, W. Härdle, & G. Stahl (Eds.), Measuring risk in complex stochastic systems. Heidelberg: Springer-Verlag.
Severini, T., & Staniswallis, J. (1994). Quasi-likelihood estimation in semiparametric models. Journal of the American Statistical Association, 89, 501–511.MathSciNetCrossRef
Severini, T., & Wong, W. (1992). Generalized profile likelihood and conditionally parametric models. Annals of Statistics, 20, 1768–1802.MathSciNetCrossRef
Metadata
Title
Nonparametric Estimators for the Probability of Default
Authors
Jürgen Franke
Wolfgang Karl Härdle
Christian Matthias Hafner
Copyright Year
2019
Book
Statistics of Financial Markets

Print ISBN: 978-3-030-13750-2

Electronic ISBN: 978-3-030-13751-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-13751-9_21