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stat.ML updates on arXiv.org

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Isotonic Recalibration under a Low Signal-to-Noise Ratio
Mario V. Wüthrich, Johanna Ziegel · 2023-01-07 · via stat.ML updates on arXiv.org

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.

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