Stochastic Models in Life Insurance

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Abstract The aim of the paper is to show how one can perform asymptotic analysis of models arising in insurance, finance, and other applications of probability theory and solve optimization problems.

Stochastic Models in Life Insurance

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    Regulatory bodies in both the European Union EU and the United States continue to propose new stochastic modeling requirements, joining efforts from other nations worldwide. Each approach permits the use of internal stochastic models. VM calls for stochastic modeling of economic risks, but does not require stochastic modeling of mortality risk however, a company may elect to do so.

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    Each of these regulations, when fully implemented, will significantly expand the use and importance of stochastic models. Insurers are expanding their use of internal stochastic models as available tools and computing power make this modeling more feasible. Companies are implementing stochastic models not only to determine economic capital, but also to use in product development areas.

    In reinsurance units, non-proportional reinsurance programs such as stop-loss and catastrophic coverages may necessitate stochastic modeling for both pricing and valuation. Need for Continued Research. Given these and other reasons for the ongoing proliferation of stochastic models, the life insurance industry still has room to expand its stochastic modeling knowledge and techniques.

    Stochastic modelling (insurance) - Wikipedia

    While the stochastic modeling of market and credit risks is fairly well established, stochastic modeling of mortality is not as fully developed. In fact, most published research regarding stochastic mortality modeling either has been across general population segments where there are no underwriting selection effects or has been conducted on longevity risks covering pensioners or annuitants. Both of these approaches pose challenges. Research on general populations, pensioners and annuitants does not carry over well to the stochastic modeling requirements of fully underwritten life insurance.

    These insured populations have distinctly different mortality characteristics that require partitioning by product, underwriting class, distribution channel, policy issue year and policy duration. Similar to deterministic modeling, such partitioning should consider the level of credibility within the partitioned segments when determining stochastic distribution metrics such as means and variances.

    intro to stochastic models

    Adjoining segments may need to be combined when segmented credibility is low. Another consideration that affects fully underwritten portfolios is policyholder lapsation. For example, lapse rates are typically very high at the end of level period for term life insurance products. This is typically not a concern when stochastically modeling general population segments or annuitants. This report evaluates stochastic modeling of life insurance nonmarket risks i.