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Estimating probabilities of default of different firms and the statistical tests

Author

Listed:
  • Amir Ahmad Dar

    (B S Abdur Rahman Crescent Institute of Science and Technology)

  • N. Anuradha

    (B S Abdur Rahman Crescent Institute of Science and Technology)

  • Shahid Qadir

    (Desh Bhagat University)

Abstract

The probability of default (PD) is the essential credit risks in the finance world. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations. Purpose This paper computes the probability of default (PD) of utilizing market-based data which outlines their convenience for monetary reconnaissance. There are numerous models that provide assistance to analyze credit risks, for example, the probability of default, migration risk, and loss gain default. Every one of these models is vital for estimating credit risk, however, the most imperative model is PD, i.e., employed in this paper. Design/methodology/approach In this paper, the Black-Scholes Model for European Call Option (BSM-CO) is utilized to gauge the PD of the Jammu and Kashmir Bank, Bank of Baroda, Indian Overseas Bank, and Canara Bank. The information has been taken from a term of 5 years on a yearly premise from 2012 to 2016. This paper demonstrates how d2 in Black Scholes displayed help in assessing the PD of the various firms. Findings The fundamental findings of this paper are whether there are any mean contrasts between the mean differences of PD between the organizations utilizing ANOVA and the Tukey strategy.

Suggested Citation

  • Amir Ahmad Dar & N. Anuradha & Shahid Qadir, 2019. "Estimating probabilities of default of different firms and the statistical tests," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:spr:jglont:v:9:y:2019:i:1:d:10.1186_s40497-019-0152-8
    DOI: 10.1186/s40497-019-0152-8
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    References listed on IDEAS

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    1. Ke Wang & Darrell Duffie, 2004. "Multi-Period Corporate Failure Prediction With Stochastic Covariates," Econometric Society 2004 Far Eastern Meetings 747, Econometric Society.
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    3. M. Tudela & G. Young, 2005. "A Merton-Model Approach To Assessing The Default Risk Of Uk Public Companies," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(06), pages 737-761.
    4. Arindam Bandyopadhyay, 2006. "Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 255-272, May.
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    6. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    7. Jessen, Cathrine & Lando, David, 2015. "Robustness of distance-to-default," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 493-505.
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    Cited by:

    1. Amir Ahmad Dar & Shahid Qadir, 2019. "Distance to default and probability of default: an experimental study," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 9(1), pages 1-12, December.
    2. Nora Gavira-Durón & Octavio Gutierrez-Vargas & Salvador Cruz-Aké, 2021. "Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation," Mathematics, MDPI, vol. 9(8), pages 1-14, April.

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