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Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Author

Listed:
  • Vitor Ozaki
  • Ralph Silva

Abstract

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Suggested Citation

  • Vitor Ozaki & Ralph Silva, 2009. "Bayesian ratemaking procedure of crop insurance contracts with skewed distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(4), pages 443-452.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:443-452
    DOI: 10.1080/02664760802474256
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    Citations

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    Cited by:

    1. Ker, Alan. P & Tolhurst, Tor & Liu, Yong, 2015. "Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205211, Agricultural and Applied Economics Association.
    2. Conradt, Sarah & Bokusheva, Raushan & Finger, Robert & Kussaiynov, Talgat, 2014. "Yield Trend Estimation in the Presence of Farm Heterogeneity and Non-linear Technological Change," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 53(2), pages 1-20, May.
    3. Park, Eunchun & Brorsen, Wade & Harri, Ardian, 2017. "Spatially Smoothed Crop Yield Density Estimation: Physical Distance vs Climate Similarity," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259145, Agricultural and Applied Economics Association.
    4. Ozaki, Vitor & Campos, Rogério, 2017. "Reduzindo a Incerteza no Mercado de Seguros: Uma Abordagem via Informações de Sensoriamento Remoto e Atuária," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(4), December.
    5. Ozaki, Vitor Augusto & Olinda, Ricardo & Faria, Priscila Neves & Campos, Rogerio Costa, 2014. "Estimation of the Agricultural Probability of Loss: evidence for soybean in Paraná Stats," Brazilian Journal of Rural Economy and Sociology (Revista de Economia e Sociologia Rural-RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 52(1), pages 1-16, March.
    6. 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.
    7. Kuangyu Wen & Ximing Wu & David J. Leatham, 2021. "Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 349-366, September.

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