The Computation of Weather-Based Index Insurance Premium and Indemnity function for Apple in Damavand County: The Application of Different Types of Elliptical and Archimedean Copulas

Document Type : Research Paper

Authors

1 Ph.D Student of Agricultural Economics, International Campus of Ferdowsi University of Mashhad, Mashhad, Iran.

2 Associate Professor of Agricultural Economics Department, Ferdowsi University of Mashhad, Mashhad, Iran.

3 Professor of Agricultural Economics Department, Ferdowsi University of Mashhad, Mashhad, Iran.

4 Assistant Professor of Agricultural Economics Department, Agricultural Planning, Economic and Rural Development Research Institute(APERDRI)

5 Assistant Professor of Agricultural Economics Department, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Considering the current challenges of agricultural insurance such as high implementation costs, problems caused by asymmetric information, and agricultural insurance’s being loss-incurring, presenting a proper insurance model is one of the most crucial issues in the field of agricultural products risk management. Thus, given the world successful experience, this study has designed the weather-based index insurance for apple in Damavand County, as one of the biggest apple production center within country. Data were collected during 1987-2016 from Iranian Agricultural Organization and the Meteorological Station in Damavand County. With the frequent application of Archimedean and elliptical copula functions in modeling the multivariate risk, dependency structure between weather variables and apple yield was determined through these functions, and Bayesian approach was employed to estimate the copula parameter. The investigation of different types of copulas indicated that Joe copula was able to model the joint distribution way better than other copulas. Hence there is a strong positive asymmetric dependency structure between variables. The expected loss caused by inappropriate weather condition has been 64281.25 ton for the Entire County. Therefore, the premium amount for each hectare at 100 percent coverage level had been computed as 36348910 Rials. The results of indemnity function demonstrated that frost is the most important loss factor for the apple product in Damavand County. Given the successful results of this insurance system in the world, it is recommended that officials and policy-makers investigate the weather based index insurance plans in different countries and identify its different dimensions to implement this insurance system in the country

Keywords

References

  1. Afrasiabi, S., & Ghahremanzade, M., Dashti., G., & Hosseinzadeh, M. (2013). Factors affecting the participation of Wheat farmers in the proposed weather based index insurance in Ahar county. Journal of Agricultural Knowledge and sustainable production,23(4), 71-84. (In Farsi).
  2. Agricultural Insurance Fund. (2013). Report on the performance of agricultural insurance fund during the recent years. Management and Planning Services. (In Farsi).
  3. Agriculture Statistics. (2015). Agriculture Ministry. Department of planning and economy, IT center. Volume 2, 1st edition. (In Farsi).
  4. Akhond, M., Kazemnezhad, A., & Hajizadeh, A. (2010). Bayesian analysis of modeled competing risks data using survival copulas. Journal of Basic Science. Islamic Azad University, 2 (78), 9-20. (In Farsi).
  5. Aziznasiri, S. (2011). Weather-based crop insurance as a viable instrument for
    agricultural risk management in Iran.     Master of Science thesis, Allameh
    Tabatabai University, E.C.O. College of Insurance. (In Farsi).
  6. Aziznasiri, S. (2012). Agricultural risk management using agricultural insurance based on weather indices. News in the world of insurance, 161, 34- 48. (In Farsi).
  7. Aziznasiri, S., Kianirad, A., & Ofoghi, R. (2012). The Determination of Weather-Based Index Insurance Premium for Agricultural Products in Iran (Case Study of Wheat of Maragheh). Eighth Bie. Conference of Agricultural Economic, Shiraz. (In Farsi).
  8. Bokusheva, R. (2010). Measuring the dependence structure between yield and weather variables. ETH Zurich, Institute for Environmental Decisions.
  9. Brechmann, E. C., & Schepsmeier, U. (2012). Modeling dependence with C- and D-vine copulas: The R-package C-D vine. To appear in the Journal of Statistical Software, 52(3), 1-27.
  10. Chen, S., Wilson, W. W., Larsen, R., & Dahl, B. (2013). Investing in Agriculture as an Asset Class. Department of Agribusiness and Applied Economics Agricultural Experiment Station North Dakota State University.
  11. Conradt, S., Robert, F., & Bokusheva, R. (2015). Tailored to the extremes: Quantile regression for index-based insurance contract design. Agricultural Economics, 46, 1-11.
  12. Czado, C., Brechmann, E. C., & Gruber, L. (2014). Selection of Vine Copulas. Technische Universitat Munchen.
  13. Daron, J. D., & Stainforth, D. A. (2014). Assessing pricing assumptions for weather index insurance in a changing climate. Climate Risk Management, 1, 76-91.
  14. Di Falco, S., Adinolfi, F., Bozzola, M., & Capitonia, F. (2014). Crop insurance as a strategy for adapting to climate change. Journal of Agricultural Economics, 65(2), 1-20.
  15. Dourandish, A., & Nikoukar, A. (2008). Comparative study of agriculture insurance policies in other countries. Iran Agriculture Insurance Fund. (In Farsi).
  16. Farzin, M., Torkamani, S., & Mousavi, N. (2012). The Role of income insurance on Darab Cotton Tiller’s risk management. Journal of Agricultural Economics Research, 4(15), 143-168. (In Farsi).
  17. Fischer, M. (2002). Tailoring copula-based multivariate generalized hyperbolic secant distributions to financial return data: An empirical investigation. Institute of Statistics and Econometrics University of Erlangen- Nurnberg, Lange Gasse 20, D-90403 Nurnberg, Germany.
  18. Flores, A.Q. (2008). Copula functions and bivariate distributions for survivalanalysis: An application to political survival. Wilf Department of Politics New York University.
  19. Goodwin, B.K., Holt, M.T., Onel, G., & Prestemon, J.P. (2011). Copula-based nonlinear models of spatial market linkages. American Journal of Agricultural Economics, in press.
  20. Hill, R.V., Hoddinott, J., & Kumar, N. (2013). Adoption of weather-index insurance: learning from willingness to pay among a panel of household in rural Ethiopia. Agricultural Economics, 44, 385-398.
  21. Iranian Agricultural Organization site, Damavand County. (2015). (In Farsi).
  22. Iranian Agricultural Organization site, Tehran Province. (2015). Available at: Tehran.agri-jahad.ir. (In Farsi)
  23. Jie, C., Li, Y., & Sijia, L. (2013). Design of Wheat drought index insurance in Shandong province. International Journal of Hybrid Information Technology, 6(4), 95-104.
  24. Karuaihe, R.N., Wang, H.H., & Young, D.L. (2006). Weather-based crop insurance contracts for African Countries. Contributed paper prepared for presentation at the International Association of Agricultural Economists Conference.
  25. Khajehpour, A., & Keykha, A. A. (2014). Evaluation of the advantages and challenges of weather-based index insurance as a modern tool in risk management of agricultural production. The ninth Bie. Conference of Iranian Agricultural Economic. May, Islamic Azad University, Karaj Branch. (In Farsi).
  26. Kochakzaei, F., & Kochakzaei, A. (2015). The study of weather-based index agriculture insurance in numerous different countries. International Conference on Applied Researches in Agriculture, Melard, Iran. Retrieved from http://www.civilica.com/Paper-ICARA01-ICARA01_085.html. (In Farsi).
  27. Kochakzaei, F., Norouzi, Gh., & Goudarzi, M. (2013). The analysis of insurance premium and paying indemnity of weather-based index agriculture insurance in Iran (case study: Razavi Khorasan province). Tehran, Iran. The 1st National Conference on Stable Agriculture and Natural Resources. Proc. of MehrArvand Higher Education Institute, Extension group of environmentalists and the Association of Iran’s nature advocacy, Retrieved from http://www.civilica.com/Paper-NACONF01-NACONF01_0520.html. (In Farsi).
  28. Kochakzaei, F., Norouzi, Gh, & Goudarzi, M. (2013). The calculation of agricultural insurance premium of rainfed wheat through precipitation index (case study: Daregaz town). Tehran, Iran. The 1st National Conference on Stable Agriculture and Natural Resources. Proc. of MehrArvand Higher Education Institute, Extension group of environmentalistsand the Association of Iran’s nature advocacy. Retrieved from http://www.civilica.com/Paper-NACONF01-NACONF01_0520.html. (In Farsi).
  29. Larsen, R., Leatham. D.J., Mjelde, J.W., & Wolfley, J.L. (2008). Geographical diversification: an application of copula based CVAR. Wolfley Texas A&M University.
  30. Leblois, A., & Quirion, P. (2010). Agricultural insurances based on meteorological indices: Realizations, Methods and Research Agenda, Downloaded from http://ideas.repec.org.
  31. Nelsen, R.B. (2006). An introduction to copulas. Second Edition.
  32. Ofoghi, R., Kianirad, A., & Aziznasiri, S. (2011). Agricultural insurance of climatic indices-based: an effective tool on agricultural risk management in Iran. AgriculturalInsurance., 8 (29-30), 25-51. (In Farsi)
  33. Pishbahar, A., Abedi, S., Dashti, G., & Kianirad, A. (2015). Weather-based crop insurance (WBCI) premium for rainfed Wheat in Miyaneh county: D-Vine copula approach application. The Journal of Agricultural Economy., 9 (3), 37-62. (In Farsi).
  34. Rao, K. N. (2010). Index based crop insurance. International conference on agricultural risk and food security 2010, Agriculture and Agricultural Science Procedia., 1, 193-203.
  35. Robison, L.J., & Barry, P.J. (1987). The competitive firm’s response to risk. New York, Macmillan.
  36. Salami, H., Ghahremanzadeh, M., Hosseini, S., &Yazdani, S. (2008). Revenue insurance an initiative to reduce production risk and price fluctuations in the country's poultry industry. Agricultural Economics, 3(4), 1-30. (In Farsi).
  37. Salami, H., & Nemati, D. (2013). Evaluation of yield systemic risk and affecting factors on its intensity for the Apple crop in Iran using autoregressive spatial model. Agricultural Economic Development, 27 (4), 288-299. (In Farsi).
  38. Skees, J.R., Varangis, P., Larson, D., & Siegel, P. (2002). Can financial markets by tapped to help poor people cope with weather risks? World Bank Policy Research Working Paper, 2577. Washington, D.C.
  39. Wenner, M., & Arias, D. (2003). Agricultural insurance in Latin America: Where are we? Paper Presented in International Conference on paving the way Forward for Rural finance.

Zhu, Y., Ghosh, S., & Goodwin, B. (2008). Modeling dependence in the design of whole farm insurance contract a copula-based approach. Contributed paper at the Annual Meeting of the AAEA, Orlando, USA, July 27-29.

Iranian Journal of Agricultural Economics and Development Research
Volume 49, Issue 1
February 2018
Pages 23-41

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