Isotonic Recalibration under a Low Signal-to-Noise Ratio
Methodology
2023-01-10 v1 Machine Learning
Statistics Theory
Computational Finance
Statistical Finance
Machine Learning
Statistics Theory
Abstract
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 ensure auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to explainable pricing systems because the resulting isotonically recalibrated regression functions have a low complexity.
Keywords
Cite
@article{arxiv.2301.02692,
title = {Isotonic Recalibration under a Low Signal-to-Noise Ratio},
author = {Mario V. Wüthrich and Johanna Ziegel},
journal= {arXiv preprint arXiv:2301.02692},
year = {2023}
}
Comments
21 pages, 9 figures