Determining Critical Yield Index of Area Yield Insurance based on Basis Risk Constraint
Â Area yield index insurance at district level faces heterogeneous basis risk due to geographical conditions which implies to obtain unprecise critical index . Clustering and zone-based area yield scheme can reduce heterogeneous basis risk that leads to determine the suitable alternative for . On the previous research, we have obtained 7 clusters and 2 level of paddy productivity based on clustering assumption from primary data in Java. The suitable clustering assumption for calculating Â is cluster based assumption, which gives the homogeneous paddy productivity under 7 clusters in Java. Therefore, our goal is to develop area yield index at district level (cluster based) with minimize basis risk at certain constraints for paddy farmer productivity in Java Indonesia. There are some methods for calculating Â such as mean, median, winsor mean, one sigma, two sigma and Â (first quartile) method on the basis risk constraints using confusion matrix. Furthermore, two basis risk constraints are the difference between overpayment and shortfall is not extremely far, and total basis risk does not exceed 20% of its total claim occurrence. Two sigma method has the lowest basis risk, overpayment, and shortfall, but it has lowest pure premium, small probability of claim, and low range of claim. Hence, we consider to use Â (first quartile) method as alternative and suitable method to calculate Â that satisfied two basis risk constraints. In conclusion, our research provides analytical calculation for area yield index at district level with pure premium as Rp 152,151 using Â (Â method), which is sufficient to cover the total claim and consistent with the simulation.
Gaurav, S., & Chaudhary, V. (2020). Do farmers care about basis risk? Evidence from a field experiment in India. Climate Risk Management, 27, 100201.
Haryastuti, R. (2020). A Study to Determine Optimum Critical Yield Index of Crop Insurance Policy in Java. Master's thesis, IPB University.
Haryastuti, R., Aidi, M. N., Pasaribu, S. M., Sumertajaya, I. M., Sutomo, V. A., Kusumaningrum, D., & Anisa, R. (2020). Cluster Based Area Yield Scheme for Crop Insurance Policy in Java. 6th International Conference on Mathematics: Pure, Applied and Computation (ICoMPAC 2020).
Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss models: from data to decisions (Vol. 715). John Wiley & Sons.
Kusumaningrum, D., Anisa, R., Sutomo, V. A., & Tan, K. S. (n.d.). Alternative Area Yield Index Based Crop Policies in Indonesia (A Presented Paper of 9th International Conference on Mathematical and Statistical Methods for Actuarial Science and Finance (MAF 2020)). Special Conference Series Volume on Mathematical and Statistical Method for Actuarial Science and Finance (eMAF2020). Switzerland: Springer Nature.
Luque, A., Carrasco, A., MartÄ±Ìn, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216â€“231.
Ministry of Agriculture. (2007). Guidelines for Farmers Institution Development. Regulation of the Ministry of Agriculture 273/Kpts/OT.160/4/2007.
Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons.
Santra, A. K., & Christy, C. J. (2012). Genetic algorithm and confusion matrix for document clustering. International Journal of Computer Science Issues (IJCSI), 9, 322.
Sutomo, V. A., Kusumaningrum, D., Anisa, R., & Paramita, A. (2019). A Bootstrap Simulation for comparison of Group Risk Plan and Multi-Peril Crop Insurance Policy. Journal of Physics: Conference Series, 1366, p. 012075.
Tinungki, G. M. (2018). The Application Law of Large Numbers That Predicts The Amount of Actual Loss in Insurance of Life. Journal of Physics: Conference Series, 979, p. 012088.
Vasanth, K., Manjunath, T. G., & Raj, S. N. (2015). A decision based unsymmetrical trimmed modified winsorized mean filter for the removal of high density salt and pepper noise in images and videos. Procedia Computer Science, 54, 595â€“604.