A Least-Squares Monte Carlo Framework in Proxy Modeling of Life Insurance Companies
Метод Монте-Карло с наименьшими квадратами для построения прокси-моделей страховых компаний, осуществляющих страхование жизни
2018-06-11
SCID: 54.1/vny42gfm
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Least-Squares Monte CarloSolvency IIlife insuranceproxy modelingsolvency capital requirement
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Abstract (AI)
The Solvency II directive asks insurance companies to derive their solvency capital requirement from the full loss distribution over the coming year. While this is in general computationally infeasible in the life insurance business, an application of the Least-Squares Monte Carlo (LSMC) method offers a possibility to overcome this computational challenge. We outline in detail the challenges a life insurer faces, the theoretical basis of the LSMC method and the necessary steps on the way to a reliable proxy modeling in the life insurance business. Further, we illustrate the advantages of the LSMC approach via presenting (slightly disguised) real-world applications.
Key Findings
1
Least-Squares Monte Carlo is presented as a way to address the computational infeasibility of deriving full one-year loss distributions for life insurers under Solvency II.
2
Slightly disguised real-world applications are used to illustrate the advantages of LSMC-based proxy modeling in life insurance.
3
The approach targets solvency capital requirement calculations that depend on the full projected loss distribution rather than limited risk summaries.
4
The framework explains the theoretical foundations and practical implementation steps required to build reliable proxy models for life insurance companies.
Research Object
Life insurance companies’ solvency-capital-requirement assessment under Solvency II
Research Subject
Reliable proxy modeling of the full one-year loss distribution using the Least-Squares Monte Carlo method to make solvency calculations computationally feasible
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2018-06-11
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