Analisa Struktur Dependensi Variabe Pembentukan Asuransi Pertanian Berbasis Indeks Cuaca dengan Multivariat Copula dan Vine Copula
The purpose of this study is to analyze the structure of the dependency on variables for calculation of insurance based on weather indices such as crop prices, yields, and rainfall. The object of research observation was secondary data on the sub-district of Dlingo Bantul District. In analyzing the dependency of variables that can be used in agricultural insurance calculations, it can be seen that both using multivariate copula and vine copula have the same results. A multivariate copula that directly looks at dependency relationships between three variables. Whereas copula vine can see the size values of the variable pair dependency for each edge in the copula vine tree. In more detail the best dependency for the grain price and rainfall variable is Copula Joe with the parameter θ = 1.76. correlation τ = 0.3. The best dependency between rainfall and yield is Frank Copula with parameters θ = 4.98 and correlation τ = 0.46. The best dependency between rainfall and yield is Frank copula with parameters θ = 2.42 and correlation τ = 0.25.
 Nasional Kontan.(Online). (2019). Hingga awal Juli, realisasi Asuransi Usaha Tani Padi (AUTP) baru capai 232.255 hektar. diaskes melalui halaman https://nasional.kontan.co.id/news/hingga-awal-juli-realisasi-asuransi-usaha-tani-padi-autp-baru-capai-232255-hektar
 Adeyinka, A. A., Krishnamurti, C., Maraseni, T. N., & Chantarat, S. (2016). The viability of weather-index insurance in managing drought risk in rural Australia. International Journal of Rural Management, 12(2), 125-142.
 Zhu, J.S., 2011. Evaluation of an Insurance Scheme Based on the Weather Index: A Case Study of Anhui Province. Chinese Economy, 44(6), pp.56-72.
 Ren, X., Li, S., Lv, C. dan Zhang, Z., 2014. Sequential dependence modeling using Bayesian theory and D-vine opula and its application on chemical process risk prediction. Industrial & Engineering Chemistry Research, 53(38), pp.14788-14801.
 Lin, J., Boyd, M., Pai, J., Porth, L., Zhang, Q., & Wang, K. (2015). Factors affecting farmers’ willingness to purchase weather index insurance in the Hainan Province of China. Agricultural Finance Review, 75(1), 103-113.
 Pishbahar, E., Abedi, S., Dashti, G. dan Kianirad, A., 2016. Measuring the Dependency Structure between yield and Weather Variabels for Ratemaking Weather-Based Crop Insurance in Ahar. European Online Journal of Natural and Social Sciences, 5(2), p.421.
 Bokusheva, R. (2011). Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 71(1), 120-141.
 Breustedt, G., Bokusheva, R., & Heidelbach, O. (2008). Evaluating the potential of index insurance schemes to reduce crop yield risk in an arid region. Journal of Agricultural Economics, 59(2), 312-328.
 Bokusheva, R. (2018). Using copulas for rating weather index insurance contracts. Journal of Applied Statistics, 45(13), 2328-2356.
 Aas, K., & Berg, D. (2010). Modeling dependence between financial returns using pair-copula constructions. In Dependence modeling: Vine copula handbook (pp. 305-328).
 Mai, J., Scherer, Matthias, & World Scientific. (2012). Simulating copulas : Stochastic models, sampling algorithms and applications (Series in quantitative finance; v. 4)
 Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and economics, 44(2), 182-198