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Assessing Credit Risk in an Agricultural Loan Portfolio

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  • Glenn D. Pederson
  • Lyubov Zech

Abstract

We show that agricultural lenders can implement a credit risk model that uses their loan portfolio data and complies with the new Basel Capital Accord without requiring Merton‐type model assumptions about underlying asset price volatility. A credit risk model is described and calibrated to the loan portfolio of a farm lender. The model is used to produce plausible estimates of expected loss, unexpected loss, and credit value‐at‐risk (VaR) at the portfolio and subportfolio (sector) levels. The lender could use these kinds of estimates to meet regulatory requirements or to adjust the level of capital in response to changing economic conditions. Nous montrons que les prêteurs agricoles peuvent appliquer un modèle de risque de crédit qui permet d'utiliser des données tirées de leur portefeuille de prêts et qui respecte le nouvel accord de Bâle sur les fonds propres, sans qu'il soit nécessaire d'utiliser les hypothèses du modèle de Merton sur la volatilité des prix des actifs. Nous avons décrit un modèle de risque de crédit et l'avons calibré en fonction du portefeuille de prêts d'un prêteur agricole. Le modèle est utilisé pour effectuer des estimations plausibles quant aux pertes prévues, aux pertes imprévues et à la valeur à risque au niveau du portefeuille et du sous‐portefeuille. Le prêteur pourrait utiliser ce genre d'estimations pour respecter les exigences réglementaires ou pour rajuster le niveau de fonds propres en fonction de l'évolution de la conjoncture économique.

Suggested Citation

  • Glenn D. Pederson & Lyubov Zech, 2009. "Assessing Credit Risk in an Agricultural Loan Portfolio," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(2), pages 169-185, June.
  • Handle: RePEc:bla:canjag:v:57:y:2009:i:2:p:169-185
    DOI: 10.1111/j.1744-7976.2009.01146.x
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    References listed on IDEAS

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    1. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
    2. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    3. Alexandre Kurth & Dirk Tasche, 2002. "Credit Risk Contributions to Value-at-Risk and Expected Shortfall," Papers cond-mat/0207750, arXiv.org, revised Nov 2002.
    4. Gordy, Michael B., 2002. "Saddlepoint approximation of CreditRisk+," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1335-1353, July.
    5. Anderson, Ross, 2004. "Agricultural Finance Markets in Transition: Credit Risk Management," Research Bulletins 301998, Cornell University, Department of Applied Economics and Management.
    6. Stam, Jerome M. & Dixon, Bruce L., 2004. "Farmer Bankruptcies And Farm Exits In The United States, 1899-2002," Agricultural Information Bulletins 33689, United States Department of Agriculture, Economic Research Service.
    7. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    8. Ani L. Katchova & Peter J. Barry, 2005. "Credit Risk Models and Agricultural Lending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 194-205.
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    Cited by:

    1. Tingqiang Chen & Suyang Wang, 2023. "Incomplete information model of credit default of micro and small enterprises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2956-2974, July.
    2. Weber, Ron & Musshoff, Oliver, 2012. "Microfinance for agricultural firms - What can we learn from bank data?," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126708, International Association of Agricultural Economists.
    3. Pederson, Glenn D. & Chu, Yu-Szu & Richardson, D. Wynn, 2011. "Community Bank Assessment of Agricultural Portfolio Risk Exposure: The Literature and the Methods in Use," Staff Papers 107483, University of Minnesota, Department of Applied Economics.
    4. Geoffroy Enjolras & Philippe Madiès, 2019. "The determinants of loan acceptance: a case study of French farms," Economics Bulletin, AccessEcon, vol. 39(1), pages 358-371.
    5. Hering, I. & Mußhoff, O., 2016. "Dynamic Incentives in Microfinance – What about the Farmers?," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 51, March.

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