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2024 | OriginalPaper | Buchkapitel

3. Credit Modeling Techniques

verfasst von : Colin Chen

Erschienen in: Practical Credit Risk and Capital Modeling, and Validation

Verlag: Springer Nature Switzerland

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Abstract

Credit risk modeling techniques become mature over more than a half century of developments. While modeling for credit risk could be traced back much earlier, theoretical affirmation of statistical models, for example, the multinomial logit model as a special case of the more general conditional logit model, was first provided about a half century ago (McFadden, 1974) using the random utility maximization paradigm. Since then, statistical models like the generalized linear models (GLM) have become the most popular selection in modeling credit risks, though machine learning models start to challenge that dominance in some areas in recent years. Figure 3.1 outlines the structure of various models discussed in this chapter.

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Vorheriges Kapitel Credit Data and Processing

Nächstes Kapitel Allowance for Credit Loss and CECL

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$$ \left\{{\displaystyle \begin{array}{cc}{b}^{\prime }{x}_i>0& {y}_i=1\\ {}{b}^{\prime }{x}_i<0& {y}_i=0\end{array}}\right. $$
Quasi-complete Separation: ∃a vector b such that:
$$ \left\{{\displaystyle \begin{array}{cc}{b}^{\prime }{x}_i\ge 0& {y}_i=1\\ {}{b}^{\prime }{x}_i\le 0& {y}_i=0\end{array}}\right. $$

with equality holds for at least one point
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Chen, C. (1993). Mean loss of prediction and its asymptotic unbiased estimators, MS Thesis, Institute of Systems Science, Academia, China.

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Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1–26.CrossRef

Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61, 101–107.CrossRef

Geisser, S. (1975). The predictive sample reuse method with applications. JASA, 70, 320–328.CrossRef

Gouriéroux, C., Monfort, A., & Trognon, A. (1984). Pseudo maximum likelihood methods: Applications to Poisson models. Econometrica, 52(3), 701–720.CrossRef

Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.CrossRef

Kalbfleisch, J. D., & Prentice, R. L. (1980). The statistical analysis of failure time data. Wiley.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics. Academic Press.

Meeker, W. Q., & Escobar, L. A. (1998). Statistical methods for reliability data. Wiley.

Moral, G. (2011). EAD estimates for facilities with explicit limits. Chapter 11. In E. Hayden, D. Porath, B. Engelmann, & R. Rauhmeier (Eds.), The Basel II risk parameters: Estimation, validation, stress testing - with applications to loan risk management. Springer-Verlag.

Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.CrossRef

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.CrossRef

Stone, M. (1974a). Cross-Validatory Choice and Assessment of Statistical Predictions (With discussion). Journal of the Royal Statistical Society Series B, 36, 111–147.CrossRef

Stone, M. (1974b). Cross-validation and multinomial prediction. Biometrika, 61, 509–515.CrossRef

Stone, M. (1977a). Asymptotics for and against cross-validation. Biometrika, 64, 29–35.CrossRef

Stone, M. (1977b). An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society Series B, 39, 44–47.CrossRef

Tobin, J. (1958). Estimation of relationship for limited dependent variables. Econometrica, 26, 24–36.CrossRef

Titel: Credit Modeling Techniques
verfasst von: Colin Chen
Verlag: Springer Nature Switzerland
Buch: Practical Credit Risk and Capital Modeling, and Validation
Print ISBN: 978-3-031-52541-4

Electronic ISBN: 978-3-031-52542-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-52542-1_3