Default and loss given default in agriculture

Glenn Pederson, Nicholas Sakaimbo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Purpose – The purpose of this paper is to investigate the relationship between loan default and loss given default (LGD) in an agricultural loan portfolio. The analysis employs a simulation model approach to evaluate the role that systematic and nonsystematic risks play in determining the economic capital requirements under different agricultural economic conditions. Design/methodology/approach – The authors employ the theoretical approach suggested by Miu and Ozdemir to assess the role of LGD in the banking industry. A Monte Carlo simulation model is developed using Excel and calibrated to an agricultural credit association using historical data. The simulation model is used to evaluate the markup to economic capital that is implied by increasing credit risks due to cyclical changes in farm real estate values. Findings – The paper demonstrates that historical systematic risks due to the correlation between probability of default (PD) and LGD through the business cycle can result in a significant markup in the economic capital required by an agricultural lender. Using historical land price changes as the driver of systematic risk, the authors show that the correlations between changing PD and land values and between the PD and LGD provide evidence of how sensitive credit risk exposure is to these parameters. Originality/value – This paper is the first application of the Miu and Ozdemir model of systematic risk to an agricultural lending institution. The model approach can be adapted by farm lenders to evaluate their changing economic capital requirements through an economic cycle in agriculture.

Original languageEnglish (US)
Pages (from-to)148-161
Number of pages14
JournalAgricultural Finance Review
Volume71
Issue number2
DOIs
StatePublished - Aug 2 2011

Keywords

  • Agriculture
  • Business cycles
  • Credit
  • Default
  • Loss
  • Simulation
  • Systematic risk

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