IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i12p205-d1546442.html
   My bibliography  Save this article

Agricultural Insurance Premium Determination Model for Risk Mitigation Based on Rainfall Index: Systematic Literature Review

Author

Listed:
  • Astrid Sulistya Azahra

    (Master Program in Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jatinangor 45363, Indonesia)

  • Muhamad Deni Johansyah

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jatinangor 45363, Indonesia)

  • Sukono

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jatinangor 45363, Indonesia)

Abstract

Rainfall is significantly essential in the agricultural sector to increase productivity. However, rainfall instability serves as a potential source of risk, causing crop failure and negatively impacting the welfare of farmers. To mitigate this risk, rainfall index-based agricultural insurance offers financial protection to farmers. There is no information on how to set a reasonable premium in index-based agricultural insurance. Therefore, this research aimed to systematically explore a model for determining a rainfall index-based agricultural insurance premium, focusing on the methods used and their effectiveness in mitigating the risk of harvest failure in the agricultural sector. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method and a bibliometric analysis were used to collect and analyze articles from Scopus, ScienceDirect, and Dimensions databases. The results showed that there were 15 articles on determining a rainfall index-based agricultural insurance premium, where 4 used the Black–Scholes method and 11 applied other main methods. Meanwhile, no articles applied the fractional Black–Scholes method in determining agricultural insurance premiums based on the rainfall index, providing new opportunities for further research. The results contributed to the development of a model for agricultural insurance premium determination that could generate more diverse and flexible premium estimates as a sustainable method to mitigate the risk of harvest failure. This research is expected to serve as a reference for developing rainfall index-based agricultural insurance in the future and contribute to the Government of the Agriculture Department’s policy formulation regarding insurance programs for farmers.

Suggested Citation

  • Astrid Sulistya Azahra & Muhamad Deni Johansyah & Sukono, 2024. "Agricultural Insurance Premium Determination Model for Risk Mitigation Based on Rainfall Index: Systematic Literature Review," Risks, MDPI, vol. 12(12), pages 1-26, December.
  • Handle: RePEc:gam:jrisks:v:12:y:2024:i:12:p:205-:d:1546442
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/12/205/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/12/12/205/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benoit Mandelbrot & Howard M. Taylor, 1967. "On the Distribution of Stock Price Differences," Operations Research, INFORMS, vol. 15(6), pages 1057-1062, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scalas, Enrico & Kaizoji, Taisei & Kirchler, Michael & Huber, Jürgen & Tedeschi, Alessandra, 2006. "Waiting times between orders and trades in double-auction markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 463-471.
    2. J. Doyne Farmer & Laszlo Gillemot & Fabrizio Lillo & Szabolcs Mike & Anindya Sen, 2004. "What really causes large price changes?," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 383-397.
    3. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    4. Saswat Patra & Malay Bhattacharyya, 2021. "Does volume really matter? A risk management perspective using cross‐country evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 118-135, January.
    5. James Caton & Richard E. Wagner, 2015. "Volatility in Catallactical Systems: Austrian Cycle Theory Revisited," Advances in Austrian Economics, in: New Thinking in Austrian Political Economy, volume 19, pages 95-117, Emerald Group Publishing Limited.
    6. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    7. Naeem, Muhammad Abubakr & Karim, Sitara & Farid, Saqib & Tiwari, Aviral Kumar, 2022. "Comparing the asymmetric efficiency of dirty and clean energy markets pre and during COVID-19," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 548-562.
    8. Xing Yang & Jun-long Mi & Jin Jiang & Jia-wen Li & Quan-shen Zhang & Meng-meng Geng, 2022. "Carbon sink price prediction based on radial basis kernel function support vector machine regression model [Chaos and order in the capital markets]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1075-1084.
    9. Beirlant, J. & Schoutens, W. & Segers, J.J.J., 2004. "Mandelbrot's Extremism," Discussion Paper 2004-125, Tilburg University, Center for Economic Research.
    10. Laura Eslava & Fernando Baltazar-Larios & Bor Reynoso, 2022. "Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model," Papers 2211.17220, arXiv.org.
    11. Kashyap, Ravi, 2019. "The perfect marriage and much more: Combining dimension reduction, distance measures and covariance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. João Costa Freitas & Alberto Adrego Pinto & Óscar Felgueiras, 2024. "Game Theory for Predicting Stocks’ Closing Prices," Mathematics, MDPI, vol. 12(17), pages 1-49, August.
    13. Meenakshi Venkateswaran & B. Wade Brorsen & Joyce A. Hall, 1993. "The distribution of standardized futures price changes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(3), pages 279-298, May.
    14. Turiel, Antonio & Pérez-Vicente, Conrad J., 2003. "Multifractal geometry in stock market time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 629-649.
    15. Xin Ling, 2017. "Normality of stock returns with event time clocks," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57, pages 277-298, April.
    16. Michele Caraglio & Fulvio Baldovin & Attilio L. Stella, 2021. "How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration," JRFM, MDPI, vol. 14(8), pages 1-15, August.
    17. Robert J. Elliott & Carlton-James U. Osakwe, 2006. "Option Pricing for Pure Jump Processes with Markov Switching Compensators," Finance and Stochastics, Springer, vol. 10(2), pages 250-275, April.
    18. Kyoung-hun Bae & Albert S. Kyle & Eun Jung Lee & Anna Obizhaeva, 2016. "Invariance of buy-sell switching points," Working Papers w0232, New Economic School (NES).
    19. Scalas, Enrico, 2006. "The application of continuous-time random walks in finance and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 225-239.
    20. Barbachan, José Fajardo & Schuschny, Andrés Ricardo & Silva, André de Castro, 2001. "Lévy processes and the Brazilian market," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 21(2), November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:12:y:2024:i:12:p:205-:d:1546442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.