Isotonic recalibration under a low signal-to-noise ratio

被引：3
|
作者：
Wuthrich, Mario V. [1 ]
Ziegel, Johanna [2 ]
机构：
[1] Swiss Fed Inst Technol, Dept Math, RiskLab, Zurich, Switzerland
[2] Univ Bern, Inst Math Stat & Actuarial Sci, Bern, Switzerland
来源：
SCANDINAVIAN ACTUARIAL JOURNAL | 2024年 / 2024卷 / 03期
关键词：
Auto-calibration; isotonic regression; isotonic recalibration; low signal-to-noise ratio; cross-financing; algorithmic solution; deep neural network; explainability; SET;
D O I：
10.1080/03461238.2023.2246743
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression models are not auto-calibrated. We propose to apply isotonic recalibration to a given regression model to restore auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to an explainable pricing system because the resulting isotonically recalibrated regression function has a low complexity.
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页码：279 / 299
页数：21
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