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Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program

Author

Listed:
  • A. Ford Ramsey

    (Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA 24061, USA)

  • Barry K. Goodwin

    (Department of Economics and Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC 27607, USA)

Abstract

The federal crop insurance program covered more than 110 billion dollars in total liability in 2018. The program consists of policies across a wide range of crops, plans, and locations. Weather and other latent variables induce dependence among components of the portfolio. Computing value-at-risk (VaR) is important because the Standard Reinsurance Agreement (SRA) allows for a portion of the risk to be transferred to the federal government. Further, the international reinsurance industry is extensively involved in risk sharing arrangements with U.S. crop insurers. VaR is an important measure of the risk of an insurance portfolio. In this context, VaR is typically expressed in terms of probable maximum loss (PML) or as a return period, whereby a loss of certain magnitude is expected to return within a given period of time. Determining bounds on VaR is complicated by the non-homogeneous nature of crop insurance portfolios. We consider several different scenarios for the marginal distributions of losses and provide sharp bounds on VaR using a rearrangement algorithm. Our results are related to alternative measures of portfolio risks based on multivariate distribution functions and alternative copula specifications.

Suggested Citation

  • A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:65-:d:223072
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yifei & Goodwin, Barry K., 2020. "Implications of U.S. Crop Insurance -- A Perspective from Copulas," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304343, Agricultural and Applied Economics Association.
    2. Marius Hofert, 2020. "Implementing the Rearrangement Algorithm: An Example from Computational Risk Management," Risks, MDPI, vol. 8(2), pages 1-28, May.
    3. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.
    4. David Edmund Allen & Elisa Luciano, 2019. "Risk Analysis and Portfolio Modelling," JRFM, MDPI, vol. 12(4), pages 1-4, September.
    5. Park, Eunchun & Harri, Ardian & Coble, Keith H., 2022. "Estimating Crop Yield Densities for Counties with Missing Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 47(3), September.

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