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A Hidden Markov Approach to Disability Insurance

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  • Boualem Djehiche
  • Björn Löfdahl

Abstract

Point and interval estimation of future disability inception and recovery rates is predominantly carried out by combining generalized linear models with time series forecasting techniques into a two-step method involving parameter estimation from historical data and subsequent calibration of a time series model. This approach may lead to both conceptual and numerical problems since any time trend components of the model are incoherently treated as both model parameters and realizations of a stochastic process. We suggest that this general two-step approach can be improved in the following way: First, we assume a stochastic process form for the time trend component. The corresponding transition densities are then incorporated into the likelihood, and the model parameters are estimated using the Expectation-Maximization algorithm. We illustrate the modeling procedure by fitting the model to Swedish disability claims data.

Suggested Citation

  • Boualem Djehiche & Björn Löfdahl, 2018. "A Hidden Markov Approach to Disability Insurance," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(1), pages 119-136, January.
  • Handle: RePEc:taf:uaajxx:v:22:y:2018:i:1:p:119-136
    DOI: 10.1080/10920277.2017.1387570
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    Cited by:

    1. Hilda Azkiyah Surya & Sukono & Herlina Napitupulu & Noriszura Ismail, 2024. "A Systematic Literature Review of Insurance Claims Risk Measurement Using the Hidden Markov Model," Risks, MDPI, vol. 12(11), pages 1-18, October.

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