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Simulation and Estimation of Loss Given Default

Author

Listed:
  • Stefan Hlawatsch

    () (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Sebastian Ostrowski

    () (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

Abstract

The aim of our paper is the development of an adequate estimation model for the loss given default, which incorporates the empirically observed bimodality and bounded nature of the distribution. Therefore we introduce an adjusted Expectation Maximization algorithm to estimate the parameters of a univariate mixture distribution, consisting of two beta distributions. Subsequently these estimations are compared with the Maximum Likelihood estimators to test the efficiency and accuracy of both algorithms. Furthermore we analyze our derived estimation model with estimation models proposed in the literature on a synthesized loan portfolio. The simulated loan portfolio consists of possibly loss-influencing parameters that are merged with loss given default observations via a quasi-random approach. Our results show that our proposed model exhibits more accurate loss given default estimators than the benchmark models for different simulated data sets comprising obligor-specific parameters with either high predictive power or low predictive power for the loss given default.

Suggested Citation

  • Stefan Hlawatsch & Sebastian Ostrowski, 2010. "Simulation and Estimation of Loss Given Default," FEMM Working Papers 100010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
  • Handle: RePEc:mag:wpaper:100010
    as

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    File URL: http://www.ww.uni-magdeburg.de/fwwdeka/femm/a2010_Dateien/2010_10.pdf
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    References listed on IDEAS

    as
    1. Jobst, Norbert J. & Zenios, Stavros A., 2005. "On the simulation of portfolios of interest rate and credit risk sensitive securities," European Journal of Operational Research, Elsevier, vol. 161(2), pages 298-324, March.
    2. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    3. Esa Jokivuolle & Samu Peura, 2003. "Incorporating Collateral Value Uncertainty in Loss Given Default Estimates and Loan‐to‐value Ratios," European Financial Management, European Financial Management Association, vol. 9(3), pages 299-314, September.
    4. Paul Glasserman & Wanmo Kang & Perwez Shahabuddin, 2008. "Fast Simulation of Multifactor Portfolio Credit Risk," Operations Research, INFORMS, vol. 56(5), pages 1200-1217, October.
    5. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bimodality; EM Algorithm; Loss Given Default; Maximum Likelihood; Mixture Distribution; Portfolio Simulation;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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