Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Time-Frequency Filtering for Seismic Waves Clustering Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A Generalized Additive Model for Binary Rare Events Data: An Application to Credit Defaults

Authors : Raffaella Calabrese, Silvia Angela Osmetti

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We aim at proposing a new model for binary rare events, i.e. binary dependent variable with a very small number of ones. We extend the Generalized Extreme Value (GEV) regression model proposed by Calabrese and Osmetti (Journal of Applied Statistics 40(6):1172–1188, 2013) to a Generalized Additive Model (GAM). We suggest to consider the quantile function of the GEV distribution as a link function in a GAM, so we propose the Generalized Extreme Value Additive (GEVA) model. In order to estimate the GEVA model, a modified version of the local scoring algorithm of GAM is proposed. Finally, to model default probability, we apply our proposal to empirical data on Italian Small and Medium Enterprises (SMEs). The results show that the GEVA model has a higher predictive accuracy to identify the rare event than the logistic additive model.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Asymmetries in Organizational Structures
next chapter The Meaning of forma in Thomas Aquinas: Hierarchical Clustering from the Index Thomisticus Treebank
Literature
Altman, E., & Sabato, G. (2006). Modeling credit risk for SMEs: evidence from the US market. Abacus, 19(6), 716–723.
Ansell, J., Lin, S., Ma, Y., & Andreeva, G. (2009, August). Experimenting with modeling default of small and medium sized enterprises (SMEs). In Credit Scoring and Credit Control XI Conference.
Basel Committee on Banking Supervision. (2005). International convergence of capital measurement and capital standards: A revised framework. Basel: Bank for International Settlements.
Calabrese, R., & Osmetti, S. A. (2013). Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model. Journal of Applied Statistics, 40(6), 1172–1188.CrossRef
Cerved Group. (2011, February). Caratteristiche delle imprese, governance e probabilità di insolvenza. Report. Milan.
Ciampi, F., & Gordini, N. (2008). Using economic-financial ratios for small enterprize default prediction modeling: an empirical analysis. In Oxford Business & Economics Conference, Oxford.
Falk, M., Haler, J., & Reiss, R. (2010). Laws of small numbers: extremes and rare events (3rd ed.). Basel: Springer.
Fantazzini, D., & Figini, S. (2009). Random survival forests models for SME credit risk measurement. Methodology and Computing in Applied Probability, 11, 29–45.CrossRefMathSciNet
Fantazzini, D., Figini, S., De Giuli, E., & Giudici P. (2009). Enhanced credit default models for heterogeneous SME segments. Journal of Financial Transformation, 25(N.1), 31–39.
Giudici, P. (2003). Applied data mining: statistical methods for business and industry. London: Wiley.
Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77, 103–123.CrossRef
Hastie, T. J., & Tibshirami, R. J. (1990). Generalized additive models. Boca Raton: Chapman & Hall.MATH
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9, 321–354.CrossRef
Kotz, S., & Nadarajah, S. (2000). Extreme value distributions. Theory and applications. London: Imperial Colleg Press.CrossRefMATH
Thomas, L., Edelman, D., & Crook, J. C. (2002). Credit scoring and its applications. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefMATH
Vozzella, P., & Gabbi, G. (2010). Default and asset correlation: An empirical study for Italian SMEs. Working Paper.
Metadata
Title
A Generalized Additive Model for Binary Rare Events Data: An Application to Credit Defaults
Authors
Raffaella Calabrese
Silvia Angela Osmetti
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_9

Premium Partner

    Image Credits