New Actuarial Science Study Findings Have Been Reported by Investigators at University of Evry (Continuous-time Semi-markov Inference Of Biometric…

New Actuarial Science Study Findings Have Been Reported by Investigators at University of Evry (Continuous-time Semi-markov Inference Of Biometric Laws Associated With A Long-term Care Insurance Portfolio)

By a News Reporter-Staff News Editor at Insurance Weekly News -- A new study on Science - Actuarial Science is now available. According to news originating from Evry, France, by VerticalNews editors, the research stated, "Unlike the mortality risk on which actuaries have been working for more than a century, the long-term care (LTC) risk is relatively new and as of today hardly mastered. Semi-Markov processes have been identified as an adequate tool to study this risk."

Our news journalists obtained a quote from the research from the University of Evry, "Nevertheless, access to data is limited and the associated literature still scarce. Insurers mainly use discrete time methods directly inspired from the study of mortality in order to build experience tables. Those methods however are not perfectly suited for the study of competing risk situations. This article provides a theoretical framework to estimate biometric laws associated with a LTC insurance portfolio. The presented method relies on a continuous-time semi-Markov model with three states: autonomy, disability and death. The process describing the state of disability is defined through its transition intensities. We provide a formula to infer the mortality of autonomous people from the mortality of the whole portfolio, on which we have more reliable knowledge. We then propose a parametric expression for the remaining intensities of the model. In particular, incidence in LTC is described by a logistic formula. Under the assumption that the disabled population is a mixture of two latent populations with respect to the category of pathology that caused LTC, we show that the resulting intensity of mortality in LTC takes a very peculiar form and depends on time spent in the LTC state. Estimation of parameters relies on the maximum likelihood method. Our parametric approach, while inducing model uncertainty, eliminates issues related to segmentation in age categories, smoothing or extrapolation at higher ages and thus proves very convenient for the practitioner."

According to the news editors, the research concluded: "Finally, we provide an application using data from a real LTC insurance portfolio."

For more information on this research see: Continuous-time Semi-markov Inference Of Biometric Laws Associated With A Long-term Care Insurance Portfolio. Astin Bulletin, 2017;47(2):527-561. Astin Bulletin can be contacted at: Cambridge Univ Press, Edinburgh Bldg, Shaftesbury Rd, CB2 8RU Cambridge, England.

The news correspondents report that additional information may be obtained from G. Biessy, Univ Evry Val dEssonne, UMR CNRS 8071, USC INRA, Lab Math & Modelisat Evry, Evry, France.

Keywords for this news article include: Evry, France, Europe, Actuarial Science, Science, Risk and Prevention, University of Evry.

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