Approximate Valuation of Life Insurance Portfolio with the Cluster Analysis: Trade-Off Between Computation Time and Precision

IF 0.3 Q4 ECONOMICS Statistika-Statistics and Economy Journal Pub Date : 2021-12-17 DOI:10.54694/stat.2021.23
János Fojtik, Jiří Procházka, Pavel Zimmermann
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Abstract

Valuation of the insurance portfolio is one of the essential actuarial tasks. Life insurance valuation is usually based on a projection of cash flows for each policy which is demanding computation time. Furthermore, modern financial management requires multiple valuations under different scenarios or input parameters. A method to reduce computation time while preserving as much accuracy as possible based on cluster analysis is presented. The basic idea of the method is to replace the original portfolio by a smaller representative portfolio based on clusters with some weights that would ensure the similarity of the valuation results to the original portfolio. Valuation is then significantly faster but requires initial time for clustering and the results are only approximate – different from the original results. The difference is studied for a different number of clusters and the trade-off between the approximation error and calculation time is evaluated.
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基于聚类分析的寿险投资组合近似估值:计算时间和精度的权衡
保险投资组合的估值是一项重要的精算任务。人寿保险估价通常基于每一份保单的现金流预测,这需要计算时间。此外,现代财务管理需要在不同的场景或输入参数下进行多次估值。提出了一种基于聚类分析的在尽可能保持精度的同时减少计算时间的方法。该方法的基本思想是用一个较小的代表性投资组合取代原始投资组合,该投资组合基于具有一定权重的聚类,以确保估值结果与原始投资组合的相似性。然后,估值明显更快,但需要初始时间进行聚类,结果只是近似的——与原始结果不同。研究了不同簇数的差异,并评估了近似误差和计算时间之间的权衡。
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
23
审稿时长
24 weeks
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