Science Journal of Applied Mathematics and Statistics

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Estimating Default Correlations Using Simulated Asset Values

Received: 07 January 2015    Accepted: 21 January 2015    Published: 02 February 2015
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Abstract

We outline the ingredients necessary to compute the Joint Default Probability from which we obtain Default Correlation, an important risk quantity in the determination of Internal Rating Based Approach in Basel II and III documents on banking supervision and regulations. We discuss Merton’s structural approach of which one key drawback is the difficulty in tracking and calibrating asset value processes and the limitations of variant models which tend to be analytically too complex and compu¬tationally intensive. We address these issues by simulating all the possible asset value processes of a firm whose asset paths we assume to be Gaussian. By generating random values that simulate all the possible asset value processes, we are able to capture all the possible default horizons within a certain macroeconomic framework. Drawing standardised normally distributed assets values of obligors we obtain a range of values of Joint Default Probabilities at a specified asset correlation from which the corresponding range of default correlations are obtained. The results is a simplified approach to the determination of default correlation, easily implementable in Excel and less analytically complicated than existing procedures.

DOI 10.11648/j.sjams.20150301.13
Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 1, February 2015)
Page(s) 14-21
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Obligor, Probability of Default, Joint Default Probability, Asset Correlation, Default Threshold, Default Correlation

References
[1] Bank for International Settlements, The New Basel Capital Accord, Consultative Document Basel Committee on Banking Supervision, pp. 84, May 2001.
[2] Black, F. and Scholes, M., “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy, Vol. 81, pp.637-654, 1973.
[3] Merton R C, On the pricing of corporate debt: the risk structure of interest Rates, Journal of Finance, Vol. 29, pp. 449-70, 1974.
[4] Jarrow, R. and Turnbull, S., “Credit Risk: Drawing the Analogy,” Risk Magazine, Vol. 5, No.9, 1992.
[5] Jarrow, R. and Turnbull, S. “Pricing Derivatives on Financial Securities Subject to Credit Risk, Journal of Finance, Vol. 50, No.1, pp. 53-85, 1995.
[6] Duffie, D. and Singleton, K, “Modeling Term Structures of Defaultable Bonds, Review of Financial Studies, Vol. 12, No.4, pp. 197-226, 1999.
[7] Bielecki, T. and Rutkowski, M. Credit Risk: Modeling, Valuation and Hedging. New York, Springer-Verlag, 2002.
[8] Rogers, L. C. G., “Modelling Credit Risk.” Working Paper, University of Bath, 1999.
[9] Lando, D., Credit Risk Modeling: Theory and Applications, Princeton University Press, NJ, 2003.
[10] Jarrow, R., vanDeventer, D., andWang, X. “A Robust Test of Merton’s Structural Model for Credit Risk Journal of Risk, Vol.6, No.1, pp. 39–58, 2003.
[11] Suresh Sundaresan, A Review of Merton’s Model of the Firm’s Capital Structure with its Wide Applications Finance & Economics Division, Columbia Business School, Columbia University, The Annual Review of Financial Economics, Vol. 5, pp. 5.1–5.21. 2013.
[12] Robert A. Jarrow, and Philip Protter, Structural Versus Reduced Form Models: A New Information Based Perspective, Journal of Investment Management, Vol. 2, No. 2, 2004.
[13] Navneet Arora, J.R. Bohn, and F. Zhu. Reduced Form vs. Structural Models of credit risk: A Case Study of three Models, Journal of Investment Management, Vol. 3 No. 4 pp. 43, 2005.
[14] Douglas J. Lucas, Default Correlation and Credit Analysis, the Journal of Fixed Income, Vol. 4, No. 4, pp. 76-87, 1995.
[15] Stefan Huschens, Konstantin Vogl, Robert Wania, Estimation of Default Probabilities and Default Correlations, pp. 239-258, 2005.
[16] Krishan Nagpal and Reza Bahar, Measuring default correlation, Risk magazine, Vol. 14, No. 3, pp. 129–132, March 2001.
[17] G. Loffler, P. N. Posch, Credit Risk Modeling using Excel and VBA, John Wiley & Sons, Chichester, England, 2007.
[18] Morten Virenfeldt, Deriving Obligor Default Correlations Using Factor Modelling, www.tools4risk.com, July 27, 2010.
[19] Equation Editor, Microsoft word 2010
Author Information
  • Mathematics and Statistics Department, Accra Polytechnic, Accra, Ghana

  • Mathematics and Statistics Department, Accra Polytechnic, Accra, Ghana

  • Research & Innovations Center, Accra Polytechnic, Accra, Ghana

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  • APA Style

    Osei Antwi, Dadzie Joseph, Louis Appiah Gyekye. (2015). Estimating Default Correlations Using Simulated Asset Values. Science Journal of Applied Mathematics and Statistics, 3(1), 14-21. https://doi.org/10.11648/j.sjams.20150301.13

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    ACS Style

    Osei Antwi; Dadzie Joseph; Louis Appiah Gyekye. Estimating Default Correlations Using Simulated Asset Values. Sci. J. Appl. Math. Stat. 2015, 3(1), 14-21. doi: 10.11648/j.sjams.20150301.13

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    AMA Style

    Osei Antwi, Dadzie Joseph, Louis Appiah Gyekye. Estimating Default Correlations Using Simulated Asset Values. Sci J Appl Math Stat. 2015;3(1):14-21. doi: 10.11648/j.sjams.20150301.13

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  • @article{10.11648/j.sjams.20150301.13,
      author = {Osei Antwi and Dadzie Joseph and Louis Appiah Gyekye},
      title = {Estimating Default Correlations Using Simulated Asset Values},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {3},
      number = {1},
      pages = {14-21},
      doi = {10.11648/j.sjams.20150301.13},
      url = {https://doi.org/10.11648/j.sjams.20150301.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjams.20150301.13},
      abstract = {We outline the ingredients necessary to compute the Joint Default Probability from which we obtain Default Correlation, an important risk quantity in the determination of Internal Rating Based Approach in Basel II and III documents on banking supervision and regulations. We discuss Merton’s structural approach of which one key drawback is the difficulty in tracking and calibrating asset value processes and the limitations of variant models which tend to be analytically too complex and compu¬tationally intensive. We address these issues by simulating all the possible asset value processes of a firm whose asset paths we assume to be Gaussian. By generating random values that simulate all the possible asset value processes, we are able to capture all the possible default horizons within a certain macroeconomic framework. Drawing standardised normally distributed assets values of obligors we obtain a range of values of Joint Default Probabilities at a specified asset correlation from which the corresponding range of default correlations are obtained. The results is a simplified approach to the determination of default correlation, easily implementable in Excel and less analytically complicated than existing procedures.},
     year = {2015}
    }
    

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    T1  - Estimating Default Correlations Using Simulated Asset Values
    AU  - Osei Antwi
    AU  - Dadzie Joseph
    AU  - Louis Appiah Gyekye
    Y1  - 2015/02/02
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    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
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    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20150301.13
    AB  - We outline the ingredients necessary to compute the Joint Default Probability from which we obtain Default Correlation, an important risk quantity in the determination of Internal Rating Based Approach in Basel II and III documents on banking supervision and regulations. We discuss Merton’s structural approach of which one key drawback is the difficulty in tracking and calibrating asset value processes and the limitations of variant models which tend to be analytically too complex and compu¬tationally intensive. We address these issues by simulating all the possible asset value processes of a firm whose asset paths we assume to be Gaussian. By generating random values that simulate all the possible asset value processes, we are able to capture all the possible default horizons within a certain macroeconomic framework. Drawing standardised normally distributed assets values of obligors we obtain a range of values of Joint Default Probabilities at a specified asset correlation from which the corresponding range of default correlations are obtained. The results is a simplified approach to the determination of default correlation, easily implementable in Excel and less analytically complicated than existing procedures.
    VL  - 3
    IS  - 1
    ER  - 

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