分析基于气候因素的农业保险与多变量coalso和Vine co挂上的农作物保险结构

A. Hidayat
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引用次数: 2

摘要

本研究的目的是分析基于农作物价格、产量和降雨量等天气指数计算保险时对变量的依赖结构。研究观察对象为德林戈班图尔区街道的二手资料。在分析农业保险计算中可以使用的变量的相关性时,可以看到,使用多元copula和藤copula都有相同的结果。直接观察三个变量之间依赖关系的多元联结公式。而copula vine可以看到copula vine树中每条边的变量对依赖的大小值。更详细地说,粮食价格和降雨变量的最佳依赖关系是Copula Joe,参数θ = 1.76。相关系数τ = 0.3。降雨量与产量之间的相关性为Frank Copula,参数θ = 4.98,相关系数τ = 0.46。降雨量与产量之间的关系为Frank copula,参数θ = 2.42,相关系数τ = 0.25。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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