Predicting probability of default of Indian corporate bonds: logistic andZ‐score model approaches

Arindam Bandyopadhyay

Journal of Risk Finance, 2006, vol. 7, issue 3, 255-272

Abstract: Purpose - This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables. Design/methodology/approach - Multiple Discriminate Analysis (MAD) is used for developingZ‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default. Findings - The newZ‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk. Originality/value - Using the newZ‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

Keywords: India; Bonds; Modelling; Emerging markets (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:15265940610664942

DOI: 10.1108/15265940610664942

Access Statistics for this article

Journal of Risk Finance is currently edited by Nawazish Mirza

More articles in Journal of Risk Finance from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().