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Measurement of Farm Credit Risk: SUR Model and Simulation Approach

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  • 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.

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File URL: http://purl.umn.edu/49222
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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49222.

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Date of creation: 2009
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Handle: RePEc:ags:aaea09:49222

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Related research

Keywords: Credit Risk Measurement; Seemingly Unrelated Regression Model; Simulation; Agribusiness; Agricultural Finance; Farm Management; Research Methods/ Statistical Methods; Risk and Uncertainty;

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Cited by:
  1. 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.

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