Bayesian Credit Ratings (new version)
AbstractIn this contribution we aim at improving ordinal variable selection in the context of causal models. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate, and, therefore, to select an effective model for classification purposes. Our proposed model is Bayesian nonparametric, and, thus, keeps the amount of model specification to a minimum. We consider the case in which information from the covariates is at the ordinal level. A noticeable instance of this regards the situation in which ordinal variables result from rankings of companies that are to be evaluated according to different macro and micro economic aspects, leading to ordinal covariates that correspond to various ratings, that entail different magnitudes of the probability of default. For each given covariate, we suggest to partition the statistical units in as many groups as the number of observed levels of the covariate. We then assume individual defaults to be homogeneous within each group and heterogeneous across groups. Our aim is to compare and, therefore, select the partition structures resulting from the consideration of different explanatory covariates. The metric we choose for variable comparison is the calculation of the posterior probability of each partition. The application of our proposal to a European credit risk database shows that it performs well, leading to a coherent and clear method for variable averaging the estimated default probabilities.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University of Pavia, Department of Economics and Management in its series DEM Working Papers Series with number 030.
Length: 19 pages
Date of creation: Jan 2013
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-02-03 (All new papers)
- NEP-ECM-2013-02-03 (Econometrics)
- NEP-RMG-2013-02-03 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Cerchiello, Paola & Giudici, Paolo, 2012. "On the distribution of functionals of discrete ordinal variables," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 2044-2049.
- A. Jorge Padilla & Marco Pagano, 1999.
"Sharing Default Information as a Borrower Discipline Device,"
CSEF Working Papers
21, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
- Padilla, A. Jorge & Pagano, Marco, 2000. "Sharing default information as a borrower discipline device," European Economic Review, Elsevier, vol. 44(10), pages 1951-1980, December.
- Padilla, A.J. & Pagano, M., 1999. "Sharing Default Information as a Borrower Discipline Device," Papers 9911, Centro de Estudios Monetarios Y Financieros-.
- Padilla, A.J. & Pagano, M., 1996. "Sharing Default Information as a Borrower Discipline Device," Papers 73, Boston University - Industry Studies Programme.
- A Jorge Padilla & Marco Pagano, 1994. "Sharing Default Information as a Borrower Discipline Device," CEPR Financial Markets Paper 0043, European Science Foundation Network in Financial Markets, c/o C.E.P.R, 77 Bastwick Street, London EC1V 3PZ.
- A. Jorge Padilla & Marco Pagano, 1996. "Sharing Default Information as a Borrower Discipline Device," Papers 0073, Boston University - Industry Studies Programme.
- Jappelli, Tullio & Pagano, Marco, 1991.
"Information Sharing in Credit Markets,"
CEPR Discussion Papers
579, C.E.P.R. Discussion Papers.
- Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
- Cornalba, Chiara & Giudici, Paolo, 2004. "Statistical models for operational risk management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 166-172.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alice Albonico).
If references are entirely missing, you can add them using this form.