Benchmarks for the benchmark approach to valuing long-term insurance liabilities: comment on Fergusson & Platen (2023)

IF 1.5 Q3 BUSINESS, FINANCE Annals of Actuarial Science Pub Date : 2023-03-01 DOI:10.1017/S1748499523000052
Daniel Bauer
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Abstract

Abstract This article comments on the paper “Less-expensive long-term annuities linked to mortality, cash and equity” by Kevin Fergusson and Eckard Platen, appearing in this issue of the Annals of Actuarial Science. It adds two perspectives to their thought-provoking contribution. The first is a similarity to some recent work in quantitative finance on “deep hedging” that leverages machine learning models to find the cheapest replication strategy for a derivative payoff in a largely model-free setting. The second perspective engages with some of the interesting implications of their approach and draws parallels to literature in asset pricing and macro-finance. These perspectives point to the potential need for more fundamental shifts than the authors of the paper are advertising.
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评估长期保险负债的基准方法的基准:对Fergusson & Platen(2023)的评论
摘要本文评论了Kevin Fergusson和Eckard Platen发表在本期《精算学年鉴》上的论文“与死亡率、现金和权益相关的较低成本长期年金”。这为他们发人深省的贡献增添了两个视角。第一个是与量化金融中最近关于“深度对冲”的一些工作相似,该工作利用机器学习模型在基本上无模型的环境中找到最便宜的衍生品回报复制策略。第二个视角涉及他们方法的一些有趣含义,并与资产定价和宏观金融方面的文献进行了比较。这些观点表明,与论文作者的广告相比,可能需要更根本的转变。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
期刊最新文献
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