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Weather Derivatives, Spatial Aggregation, and Systemic Risk: Implications for Reinsurance Hedging

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  • Woodard, Joshua D.
  • Garcia, Philip

Abstract

Previous studies identify limited potential efficacy of weather derivatives in hedging agricultural exposures. In contrast to earlier studies which investigate the problem at low levels of aggregation, we find that better weather hedging opportunities may exist at higher levels of spatial aggregation. Aggregating production exposures reduces idiosyncratic risk, leaving a greater proportion of the total risk in the form of systemic weather risk which can be effectively hedged using relatively simple weather derivatives. The aggregation effect suggests that the potential for weather derivatives in agriculture may be greater than previously thought, particularly for aggregators of risk such as reinsurers.

Suggested Citation

  • Woodard, Joshua D. & Garcia, Philip, 2008. "Weather Derivatives, Spatial Aggregation, and Systemic Risk: Implications for Reinsurance Hedging," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(1), pages 1-18, April.
    • Handle: RePEc:ags:jlaare:36705
    DOI: 10.22004/ag.econ.36705
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    2. Hongyun Han & Ye Jiang, 2019. "Systemic Risks of Climate Events and Households’ Participation in Mariculture Mutual Insurance: A Case Study of Shrimp Producers in Zhejiang Province," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    3. Gülpınar, Nalân & Çanakoḡlu, Ethem, 2017. "Robust portfolio selection problem under temperature uncertainty," European Journal of Operational Research, Elsevier, vol. 256(2), pages 500-523.
    4. Woodard, Joshua D. & Chiu Verteramo, Leslie & Miller, Alyssa P., 2015. "Adaptation of U.S. Agricultural Production to Drought and Climate Change," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205903, Agricultural and Applied Economics Association.
    5. 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.
    6. Tappi, Marco & Nardone, Gianluca & Santeramo, Fabio Gaetano, 2022. "On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 11(2), July.
    7. Gong, Xuche & Feng, Hongli & Hennessy, David A., 2018. "Systemic Risk, Geography and Area Insurance," 2018 Annual Meeting, August 5-7, Washington, D.C. 274479, Agricultural and Applied Economics Association.
    8. Li, Hong & Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2021. "Improved index insurance design and yield estimation using a dynamic factor forecasting approach," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 208-221.
    9. Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.
    10. Chung, Wonho, 2013. "Reducing the Social Cost of Federal Crop Insurance: A Role for US Government Hedging with Weather Derivatives," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 36(2), pages 1-26, August.
    11. Hennessy, David A., 2011. "Modeling Stochastic Crop Yield Expectations with a Limiting Beta Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-15, April.

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