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Measurement of Farm Credit Risk: SUR Model and Simulation Approach

Author

Listed:
  • Yan, Yan
  • Barry, Peter J.
  • Paulson, Nicholas D.
  • Schnitkey, Gary D.

Abstract

The study addresses problems in measuring credit risk under the structure model, and then proposes a seemingly unrelated regression model (SUR) to predict farms’ ability in meeting their current and anticipated obligations in the next 12 months. The empirical model accounts for both the dependence structure and the dynamic feature of the structure model, and is used for estimating asset correlation using FBFM data for 1995-2004. Farm credit risk is then predicted by copula based simulation process with historical default rates as benchmark. Results are reported and compared to previous studies on farm default.

Suggested Citation

  • Yan, Yan & Barry, Peter J. & Paulson, Nicholas D. & Schnitkey, Gary D., 2009. "Measurement of Farm Credit Risk: SUR Model and Simulation Approach," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49222, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea09:49222
    DOI: 10.22004/ag.econ.49222
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    Cited by:

    1. Frederick Murdoch Quaye & Denis Nadolnyak & Valentina Hartarska, 2017. "Factors Affecting Farm Loan Delinquency in the Southeast," Research in Applied Economics, Macrothink Institute, vol. 9(4), pages 75-92, December.
    2. Paolo Guarda & Abdelaziz Rouabah & John Theal, 2011. "An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests," BCL working papers 63, Central Bank of Luxembourg.

    More about this item

    Keywords

    Agribusiness; Agricultural Finance; Farm Management; Research Methods/ Statistical Methods; Risk and Uncertainty;
    All these keywords.

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