Embedded Predictor Selection for Default Risk Calculation: A Southeast Asian Industry Study

Wolfgang Karl Härdle*, Dedy Dwi Prastyo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)

Abstract

Default risk estimation is a core business of rating agencies. Banks and other financial institutions need to scale the default risk of their counterparties, identifying predictors that significantly contribute to default probability insight into fundamentals of credit risk analysis. Default analysis and predictor selection are two related issues, but many existing approaches address them separately. A unified procedure is employed, a regularization approach based on the GLM model, which allows simultaneously selecting the default predictors and optimizes all the parameters within the model. For this purpose Lasso and elastic-net penalty functions are employed as regularization terms. The methods are applied to predict default of companies from the industry sector in Southeast Asian countries. The relevant default predictors over the countries reveal that credit risk analysis is sample specific. The empirical result shows that the proposed method has a very high accuracy of default prediction.

Original languageEnglish
Title of host publicationHandbook of Asian Finance
Subtitle of host publicationFinancial Markets and Sovereign Wealth Funds
PublisherElsevier Inc.
Pages131-148
Number of pages18
Volume1
ISBN (Electronic)9780128011010
ISBN (Print)9780128009826
DOIs
Publication statusPublished - 4 Jun 2014

Keywords

  • Default risk
  • Elastic-net
  • Lasso
  • Logit
  • Predictor selection
  • Regularization

Fingerprint

Dive into the research topics of 'Embedded Predictor Selection for Default Risk Calculation: A Southeast Asian Industry Study'. Together they form a unique fingerprint.

Cite this