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Estimating VaR in credit risk: Aggregate vs single loss distribution

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  • M. Assadsolimani
  • D. Chetalova

Abstract

Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a possible risk measure requires adequate techniques. One of these techniques is the application of a compound distribution for the aggregates in a portfolio. In this paper, we consider the aggregated loss of Gamma distributed severities and estimate the VaR by introducing a new approach to calculate the quantile function of the Gamma distribution at high confidence levels. We then compare the VaR obtained from the aggregation process with the VaR obtained from a single loss distribution where the severities are drawn first from an exponential and then from a truncated exponential distribution. We observe that the truncated exponential distribution as a model for the severities yields results closer to those obtained from the aggregation process. The deviations depend strongly on the number of obligors in the portfolio, but also on the amount of gross loss which truncates the exponential distribution.

Suggested Citation

  • M. Assadsolimani & D. Chetalova, 2017. "Estimating VaR in credit risk: Aggregate vs single loss distribution," Papers 1702.04388, arXiv.org.
    • Handle: RePEc:arx:papers:1702.04388
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    References listed on IDEAS

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    1. Asad Munir & William Shaw, 2012. "Quantile Mechanics 3: Series Representations and Approximation of some Quantile Functions appearing in Finance," Papers 1203.5729, arXiv.org, revised Apr 2012.
    2. Alexandre Hocquard & Nicolas Papageorgiou & Bruno Remillard, 2015. "The payoff distribution model: an application to dynamic portfolio insurance," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 299-312, February.
    3. Lindskog, Filip & McNeil, Alexander J., 2003. "Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 209-238, November.
    4. De Pril, Nelson, 1985. "Recursions for Convolutions of Arithmetic Distributions," ASTIN Bulletin, Cambridge University Press, vol. 15(2), pages 135-139, November.
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