Bayesian Credit Rating Assessment
In 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 different ordinal covariates that correspond to different 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 to explain method for variable averaging the estimated default probabilities.
|Date of creation:||Nov 2012|
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- 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.
- Padilla, A.J. & Pagano, M., 1996.
"Sharing Default Information as a Borrower Discipline Device,"
73, Boston University - Industry Studies Programme.
- 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.
- 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, 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.
- A. Jorge Padilla & Marco Pagano, 1996. "Sharing Default Information as a Borrower Discipline Device," Papers 0073, Boston University - Industry Studies Programme.
- Padilla, A.J. & Pagano, M., 1999. "Sharing Default Information as a Borrower Discipline Device," Papers 9911, Centro de Estudios Monetarios Y Financieros-.
- Pagano, Marco & Jappelli, Tullio, 1993.
" Information Sharing in Credit Markets,"
Journal of Finance,
American Finance Association, vol. 48(5), pages 1693-1718, December.
- 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.
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