Paper ID: 2301.02692

Isotonic Recalibration under a Low Signal-to-Noise Ratio

Mario V. Wüthrich, Johanna Ziegel

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.

Submitted: Jan 6, 2023