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Tailored to the extremes: Quantile regression for index-based insurance contract design

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  • Sarah Conradt
  • Robert Finger
  • Raushan Bokusheva

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  • Sarah Conradt & Robert Finger & Raushan Bokusheva, 2015. "Tailored to the extremes: Quantile regression for index-based insurance contract design," Agricultural Economics, International Association of Agricultural Economists, vol. 46(4), pages 537-547, July.
    • Handle: RePEc:bla:agecon:v:46:y:2015:i:4:p:537-547
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    1. Babcock, Bruce A. & Choi, E. Kwan & Feinerman, Eli, 1993. "Risk And Probability Premiums For Cara Utility Functions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 18(1), pages 1-8, July.
    2. Barnwal, Prabhat & Kotani, Koji, 2013. "Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India," Ecological Economics, Elsevier, vol. 87(C), pages 95-109.
    3. John M. Antle, 1987. "Econometric Estimation of Producers' Risk Attitudes," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(3), pages 509-522.
    4. Calum G. Turvey, 2001. "Weather Derivatives for Specific Event Risks in Agriculture," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 23(2), pages 333-351.
    5. Robert G. Chambers, 1989. "Insurability and Moral Hazard in Agricultural Insurance Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(3), pages 604-616.
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    Cited by:

    1. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    2. Lamonaca, Emilia & Santeramo, Fabio Gaetano & Seccia, Antonio, 2021. "Climate changes and new productive dynamics in the global wine sector," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 10(2), April.
    3. Eltazarov, Sarvarbek & Bobojonov, Ihtiyor & Kuhn, Lena & Glauben, Thomas, 2021. "Mapping weather risk – A multi-indicator analysis of satellite-based weather data for agricultural index insurance development in semi-arid and arid zones of Central Asia," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23.
    4. 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.
    5. Tobias Dalhaus & Barry J Barnett & Robert Finger, 2020. "Behavioral weather insurance: Applying cumulative prospect theory to agricultural insurance design under narrow framing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-25, May.
    6. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2021. "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317075, German Association of Agricultural Economists (GEWISOLA).
    7. Wienand Kölle & Andrea Martínez Salgueiro & Matthias Buchholz & Oliver Musshoff, 2021. "Can satellite‐based weather index insurance improve the hedging of yield risk of perennial non‐irrigated olive trees in Spain?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(1), pages 66-93, January.
    8. Möllmann, Johannes & Buchholz, Matthias & Kölle, Wienand & Musshoff, Oliver, 2020. "Do remotely-sensed vegetation health indices explain credit risk in agricultural microfinance?," World Development, Elsevier, vol. 127(C).
    9. Djanibekov, Utkur & Finger, Robert, 2018. "Agricultural risks and farm land consolidation process in transition countries: The case of cotton production in Uzbekistan," Agricultural Systems, Elsevier, vol. 164(C), pages 223-235.
    10. Matthieu Stigler & Apratim Dey & Andrew Hobbs & David Lobell, 2022. "With big data come big problems: pitfalls in measuring basis risk for crop index insurance," Papers 2209.14611, arXiv.org.
    11. Vroege, Willemijn & Dalhaus, Tobias & Finger, Robert, 2019. "Index insurances for grasslands – A review for Europe and North-America," Agricultural Systems, Elsevier, vol. 168(C), pages 101-111.
    12. Nordmeyer, Eike Florenz & Danne, Michael & Musshoff, Oliver, 2023. "Can satellite-retrieved data increase farmers' willingness to insure against drought? – Insights from Germany," Agricultural Systems, Elsevier, vol. 211(C).
    13. Tappi, Marco & Carucci, Federica & Gatta, Giuseppe & Giuliani, Marcella Michela & Lamonaca, Emilia & Santeramo, Fabio Gaetano, 2023. "Temporal and design approaches and yield-weather relationships," MPRA Paper 117488, University Library of Munich, Germany.
    14. Matthieu Stigler & David Lobell, 2021. "Optimal index insurance and basis risk decomposition: an application to Kenya," Papers 2111.08601, arXiv.org, revised Mar 2023.
    15. Daniel Lima Miquelluti & Vitor Augusto Ozaki & David José Miquelluti, 2022. "An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 26(3), pages 200387-2003.
    16. Lima Miquelluti, Daniel & Ozaki, Vitor & Miquelluti, David J., 2020. "An application of geographically weighted quantile LASSO to weather index insurance design," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304288, Agricultural and Applied Economics Association.
    17. Shih‐Chieh Liao & Shih‐Chieh Chang & Tsung‐Chi Cheng, 2022. "Index‐based renewable energy insurance for Taiwan Solar Photovoltaic Power Plants," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(2), pages 145-172, June.

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