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Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches


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  • Arindam Bandyopadhyay


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 developing Z-score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default. Findings – The new Z-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 new Z-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.

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Bibliographic Info

Article provided by Emerald Group Publishing in its journal Journal of Risk Finance.

Volume (Year): 7 (2006)
Issue (Month): 3 (May)
Pages: 255-272

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Handle: RePEc:eme:jrfpps:v:7:y:2006:i:3:p:255-272

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Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK

Related research

Keywords: Bonds; Emerging markets; India; Modelling;


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Cited by:
  1. Abbas, Qaiser & Rashid, Abdul, 2011. "Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan," MPRA Paper 28161, University Library of Munich, Germany.
  2. Alexeev, Michael & Kim, Jounghyeon, 2012. "Bankruptcy and institutions," Economics Letters, Elsevier, vol. 117(3), pages 676-678.
  3. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
  4. MK, Sinha & JP, Dhaka, 2013. "Predicting risk of credit default using discriminant aproach:A study of tribal dairy darmers from Jharkhand," MPRA Paper 54158, University Library of Munich, Germany.
  5. Sinha, Mukesh Kumar & Dhaka, J. P. & Mondal, B., 2013. "Analysing social attributes of loan default among small Indian Dairy farms: A discriminating approach," MPRA Paper 53362, University Library of Munich, Germany, revised 20 Jan 2014.


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