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一种用于对可变年金大型投资组合进行估值的数据挖掘框架
A Data Mining Framework for Valuing Large Portfolios of Variable Annuities |
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| 课程网址: |
http://videolectures.net/kdd2017_gan_data_mining/
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| 主讲教师: |
Guojun Gan
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| 开课单位: |
视频讲座网
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| 开课时间: |
2017-10-09
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| 课程语种: |
英语
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| 中文简介: |
可变年金是一种延迟缴税的退休工具，旨在解决许多人对资产寿命过长的担忧。在过去10年里，可变年金的快速增长给保险公司带来了巨大挑战，尤其是在评估这些产品中所包含的复杂担保时。
在本文中，我们提出了一个数据挖掘框架来解决与可变年金合约的大型投资组合估值相关的计算问题。该数据挖掘框架由两个主要部分组成:一个是用于选择具有代表性的可变年金合同的数据聚类算法，另一个是用于根据具有代表性的合同预测整个投资组合的利息数量的回归模型。在一个综合可变年金合约组合上进行了一系列的数值实验，以证明我们提出的数据挖掘框架在准确性和速度方面的性能。实验结果表明，与现有的方法相比，我们提出的框架能够产生对各种兴趣量的准确估计，并能显著降低运行时间。
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| 课程简介: |
A variable annuity is a tax-deferred retirement vehicle created to address concerns that many people have about outliving their assets. In the past decade, the rapid growth of variable annuities has posed great challenges to insurance companies especially when it comes to valuing the complex guarantees embedded in these products.
In this paper, we propose a data mining framework to address the computational issue associated with the valuation of large portfolios of variable annuity contracts. The data mining framework consists of two major components: a data clustering algorithm which is used to select representative variable annuity contracts, and a regression model which is used to predict quantities of interest for the whole portfolio based on the representative contracts. A series of numerical experiments are conducted on a portfolio of synthetic variable annuity contracts to demonstrate the performance of our proposed data mining framework in terms of accuracy and speed. The experimental results show that our proposed framework is able to produce accurate estimates of various quantities of interest and can reduce the runtime significantly compared to the state-of-the-art approaches.
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| 关 键 词: |
延迟缴税; 可变年金; 资产寿命; 评估担保
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-11-20：chenxin01
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| 最后编审: |
2023-05-18：liyy
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| 阅读次数: |
44
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