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An Empirical Study of Exposure at Default

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  • Michael Jacobs Jr

Abstract

In this study we empirically investigate the determinants of and build a predictive econometric model for exposure at default EAD using a sample of Moody s rated defaulted firms having revolving credits We extend prior empirical work by considering alternative determinants of EAD risk in addition to the traditional factors e g credit rating Various measures of EAD risk are derived and compared We build a multiple regression model in the generalized linear class and examine the comparative rank ordering and predictive accuracy properties of these We find weak evidence of counter cyclicality in EAD While we find EAD risk to decrease with default risk utilization has the strongest inverse relation We also find EAD risk reduced for greater leverage liquidity more debt cushion and increased for greater company size higher collateral rank or more bank debt in the capital structure of the defaulted obligor The models are validated rigorously through resampling experiment in a rolling out of time and sample experiment In addition to the credit risk management implications of this study the parameterization of pricing and portfolio management models there is use in quantifying EAD risk for banks qualifying for the Advanced IRB approach in the regulatory framework of the Basel II accord

Suggested Citation

  • Michael Jacobs Jr, 2010. "An Empirical Study of Exposure at Default," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 1(1), pages 31-59.
    • Handle: RePEc:srs:jasf00:v:1:y:2010:i:1:p:31-59
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    Cited by:

    1. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2021. "Determinants of corporate exposure at default under distressed economic and financial conditions in a developing economy: the case of Zimbabwe," Risk Management, Palgrave Macmillan, vol. 23(1), pages 123-149, June.
    2. Shan Luo & Anthony Murphy, 2020. "Understanding the Exposure at Default Risk of Commercial Real Estate Construction and Land Development Loans," Working Papers 2007, Federal Reserve Bank of Dallas.
    3. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    4. Grundke, Peter & Kühn, André, 2020. "The impact of the Basel III liquidity ratios on banks: Evidence from a simulation study," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 167-190.
    5. Gürtler, Marc & Hibbeln, Martin Thomas & Usselmann, Piet, 2018. "Exposure at default modeling – A theoretical and empirical assessment of estimation approaches and parameter choice," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 176-188.
    6. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "A mixture model for credit card exposure at default using the GAMLSS framework," International Journal of Forecasting, Elsevier, vol. 39(1), pages 503-518.
    7. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.

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