交叉分类信誉模型的 R 软件包:不良贷款的应用

Seda Tuğçe Altan, Muhlis Özdemi̇r, M. Ebegil
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引用次数: 0

摘要

可信度理论用于确定非寿险部门的保费,是一种计算方法,用于对过去和近期数据之间的平衡分配进行加权估算。加权程序是通过 Z 可信系数完成的。有多种方法被称为可信度模型来确定 Z 值。其中一个模型是 Dannenburg(1995 年)提出的交叉分类可信度模型。在该模型中,保险组合被两个定性风险因素细分,以对称方式建模。尤其是当数据无法分层分类时,该模型提供了另一种方法。同时,该模型考虑了风险因素的联合效应和分离效应。要预测该模型中的保费,必须计算通过求解线性方程组得到的方差分量。然而,该方程组无法显式求解。此外,保费估算必须计算过多的参数。在这里,可能会出现计算错误,而且很难找到正确的结果。此外,目前还没有一种工具可以在计算机上轻松完成这些操作。本研究开发了 R 软件包 cccm,以方便、快捷、准确地计算交叉分类可信度模型的结构参数。cccm软件包为有兴趣解决交叉分类可信度问题的用户提供了逐步说明。
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An R Package For Crossed Classification Credibility Model: Application Regarding Non-Performing Loan
Credibility theory, which is used to determine the premium in the non-life branches of insurance, is a calculation method which is used for making weighted estimation of balanced allocation between past and recent period data. The procedure of weighting is done with the Z credibility factor. There are miscellaneous methods which are named as credibility models to determine Z value. One of these models is Crossed Classification Credibility Model, which is introduced by Dannenburg (1995). In this model, an insurance portfolio is subdivided by two qualitative risk factors, modeled in symmetrical way. Especially, this model offers an alternative method when data are unclassifiable hierarchically. Simultaneously, this model considers the joint and separates the effects of risk factors. To predict the premiums in this model, variance components are obtained by solving the linear equation system must be calculated. However, this system cannot be solved explicitly. Also, too many parameters must be calculated for the premium estimation. Here, calculation errors can occur, and it is very difficult to find the correct results. Moreover, there is no tool that can easily perform these operations on a computer. In this study, the R package cccm has been developed to calculate the structural parameters easily, quickly, and accurately for Crossed Classification Credibility Model. Package cccm explained step by step for the users interested in to solve Crossed Classification Credibility problems.
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