Multivariate Ordinal Regression Models: An Analysis of Corporate Credit Ratings

Hirk, Rainer and Hornik, Kurt ORCID: and Vana, Laura (2017) Multivariate Ordinal Regression Models: An Analysis of Corporate Credit Ratings. Research Report Series / Department of Statistics and Mathematics, 132. WU Vienna University of Economics and Business, Vienna.


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Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.

Item Type: Paper
Keywords: composite likelihood, credit ratings, financial ratios, latent variable models, multivariate ordered probit, multivariate ordered logit
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Laura Vana
Date Deposited: 24 Jan 2017 08:40
Last Modified: 24 Oct 2019 13:41


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