A robust random coefficient regression representation of the chain-ladder method

IF 1.5 Q3 BUSINESS, FINANCE Annals of Actuarial Science Pub Date : 2021-06-09 DOI:10.1017/S1748499521000154
Ioannis Badounas, Apostolos Bozikas, G. Pitselis
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引用次数: 2

Abstract

Abstract It is well known that the presence of outliers can mis-estimate (underestimate or overestimate) the overall reserve in the chain-ladder method, when we consider a linear regression model, based on the assumption that the coefficients are fixed and identical from one observation to another. By relaxing the usual regression assumptions and applying a regression with randomly varying coefficients, we have a similar phenomenon, i.e., mis-estimation of the overall reserves. The lack of robustness of loss reserving regression with random coefficients on incremental payment estimators leads to the development of this paper, aiming to apply robust statistical procedures to the loss reserving estimation when regression coefficients are random. Numerical results of the proposed method are illustrated and compared with the results that were obtained by linear regression with fixed coefficients.
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链梯法的一种稳健随机系数回归表示
摘要众所周知,在链梯法中,当我们考虑线性回归模型时,基于系数固定且从一个观测到另一个观测相同的假设,异常值的存在可能会错误估计(低估或高估)总储量。通过放松通常的回归假设并应用系数随机变化的回归,我们也出现了类似的现象,即对总储量的错误估计。具有随机系数的损失准备金回归对增量支付估计量缺乏稳健性,这导致了本文的发展,旨在将稳健统计程序应用于回归系数为随机时的损失准备金估计。给出了该方法的数值结果,并与固定系数线性回归的结果进行了比较。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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