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Modeling a Distribution of Mortgage Credit Losses

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Abstract

One of the biggest risks arising from financial operations is the risk of counterparty default, commonly known as a “credit risk”. Leaving unmanaged, the credit risk would, with a high probability, result in a crash of a bank. In our paper, we will focus on the credit risk quantification methodology. We will demonstrate that the current regulatory standards for credit risk management are at least not perfect, despite the fact that the regulatory framework for credit risk measurement is more developed than systems for measuring other risks, e.g. market risks or operational risk. Generalizing the well known KMV model, standing behind Basel II, we build a model of a loan portfolio involving a dynamics of the common factor, influencing the borrowers’ assets, which we allow to be non-normal. We show how the parameters of our model may be estimated by means of past mortgage deliquency rates. We give a statistical evidence that the non-normal model is much more suitable than the one assuming the normal distribution of the risk factors. We point out how the assumption that risk factors follow a normal distribution can be dangerous. Especially during volatile periods comparable to the current crisis, the normal distribution based methodology can underestimate the impact of change in tail losses caused by underlying risk factors.

Suggested Citation

  • Petr Gapko & Martin Šmíd, 2010. "Modeling a Distribution of Mortgage Credit Losses," Working Papers IES 2010/23, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2010.
  • Handle: RePEc:fau:wpaper:wp2010_23
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    File URL: http://ies.fsv.cuni.cz/default/file/download/id/14476
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    References listed on IDEAS

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    1. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option Pricing under GARCH models with Generalized Hyperbolic distribution (II) : Data and Results," Post-Print hal-00308687, HAL.
    2. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option pricing under GARCH models with generalized hyperbolic innovations (I): methodology," Documents de travail du Centre d'Economie de la Sorbonne b08037, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    3. Tomas Havranek & Zuzana Irsova, 2010. "Which Foreigners Are Worth Wooing? A Meta-Analysis of Vertical Spillovers from FDI," Working Papers 2010/03, Czech National Bank.
    4. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option Pricing under GARCH models with Generalized Hyperbolic distribution (II) : Data and Results," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00308687, HAL.
    5. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option Pricing under GARCH models with Generalized Hyperbolic innovations (I) : Methodology," Post-Print halshs-00281585, HAL.
    6. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option pricing under GARCH models with generalized hyperbolic innovations (II): data and results," Documents de travail du Centre d'Economie de la Sorbonne b08047, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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    Cited by:

    1. Petr Gapko & Martin Smid, 2016. "Multi-Period Structural Model of a Mortgage Portfolio with Cointegrated Factors," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 565-574, December.

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    More about this item

    Keywords

    Credit Risk; Mortgage; Delinquency Rate; Generalized Hyperbolic Distribution; Normal Distribution;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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