Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance

Mark Kiermayer and Christian Wei{\ss}

Papers from arXiv.org

Abstract: Markov chains play a key role in a vast number of areas, including life insurance mathematics. Standard actuarial quantities as the premium value can be interpreted as compressed, lossy information about the underlying Markov process. We introduce a method to reconstruct the underlying Markov chain given collective information of a portfolio of contracts. Our neural architecture explainably characterizes the process by explicitly providing one-step transition probabilities. Further, we provide an intrinsic, economic model validation to inspect the quality of the information decompression. Lastly, our methodology is successfully tested for a realistic data set of German term life insurance contracts.

Date: 2022-01
New Economics Papers: this item is included in nep-cmp, nep-cta, nep-ias and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2201.02397

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