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Banana Production Risk Zoning and Copula Based Income Insurance Rate Determination: a Case Study of Guangdong, China

Author

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  • Jianming Xie

    (Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand)

  • Charuk Singhapreecha

    (Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand)

  • Warattaya Chinnakum

    (Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand)

  • Kittawit Autchariyapanitkul

    (Faculty of Economics, Maejo University, Chiang Mai 50290, Thailand)

Abstract

This study explores the risk characteristics and regional differences in banana production in 21 cities in Guangdong Province through cluster analysis and kernel density estimation. Based on statistical analysis and optimal clustering number selection, cities are divided into three categories: high, medium, and low-risk areas, reflecting the characteristics of resource dependent, economic core, and technological innovation cities, respectively. The estimation of kernel density function reveals the concentration and tail characteristics of production and price distribution in different risk areas. By analyzing the dependence relationship between price and output through the Copula model, t Copula and Frank Copula performed well in high-risk and medium risk areas, respectively. The insurance pricing results show that the premium rate in high-risk areas is more sensitive to changes in coverage, reflecting the significant impact of differences in risk distribution on insurance design. This study provides important references for regional risk management and insurance product optimization.

Suggested Citation

  • Jianming Xie & Charuk Singhapreecha & Warattaya Chinnakum & Kittawit Autchariyapanitkul, 2025. "Banana Production Risk Zoning and Copula Based Income Insurance Rate Determination: a Case Study of Guangdong, China," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 3816-3828, January.
    • Handle: RePEc:bcp:journl:v:9:y:2025:i:1:p:3816-3828
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    References listed on IDEAS

    as
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    2. Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    3. Kularatne, Thilini Dulanjali & Li, Jackie & Pitt, David, 2021. "On the use of Archimedean copulas for insurance modelling," Annals of Actuarial Science, Cambridge University Press, vol. 15(1), pages 57-81, March.
    4. Peng Shi & Gee Y. Lee, 2022. "Copula Regression for Compound Distributions with Endogenous Covariates with Applications in Insurance Deductible Pricing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1094-1109, September.
    5. Weiwei Guo & Tao Wu & Guojun Jiang & Lijie Pu & Jianzhen Zhang & Fei Xu & Hongmei Yu & Xuefeng Xie, 2021. "Spatial Distribution, Environmental Risk and Safe Utilization Zoning of Soil Heavy Metals in Farmland, Subtropical China," Land, MDPI, vol. 10(6), pages 1-13, May.
    6. Tiwari, Sweta & Coble, Keith H. & Barnett, Barry J. & Harri, Ardian, 2021. "Hedging the Price Risk Inherent in Revenue Protection Insurance," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 53(4), pages 510-530, November.
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