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

arXiv 2201.02397 ↗First posted 7 Jan 2022cs.LG q-fin.STcited by 0

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Abstract (arXiv)

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.

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References · 18

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6 more
  1. 2020A Convolutional Deep Markov Model for Unsupervised Speech Representation Learningcited by 18
  2. 2019Powering Hidden Markov Model by Neural Network based Generative Modelscited by 7
  3. 2018Markov Chain Neural Networkscited by 6
  4. 2018Hierarchical Reinforcement Learning Framework towards Multi-agent Navigationcited by 6
  5. 2020Reinforced Deep Markov Models With Applications in Automatic Tradingcited by 5
  6. 2020Estimation of Semi-Markov Multi-state Models: A Comparison of the Sojourn Times and Transition Intensities Approachescited by 3

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