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An insurance and investment portfolio model using chance constrained programming

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  • Li, S. X.

Abstract

An insurance and investment portfolio model is here formulated in terms of the 'P-Models' of Chance Constrained Programming, which is then related to the 'satisficing concepts' of Simon. For a given insurers' aspiration level of return on equity and risk levels of violating minimum requirements on return and on cash and liquid assets, we propose a method to maximize the insurers' probability of achieving their aspiration level, subject to two chance constraints and other regulatory and institutional constraints. An empirical example is given, based on the industry's aggregated data for a twenty year period.

Suggested Citation

  • Li, S. X., 1995. "An insurance and investment portfolio model using chance constrained programming," Omega, Elsevier, vol. 23(5), pages 577-585, October.
    • Handle: RePEc:eee:jomega:v:23:y:1995:i:5:p:577-585
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
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    5. N. H. Agnew & R. A. Agnew & J. Rasmussen & K. R. Smith, 1969. "An Application of Chance Constrained Programming to Portfolio Selection in a Casualty Insurance Firm," Management Science, INFORMS, vol. 15(10), pages 512-520, June.
    6. Howard E. Thompson & John P. Matthews & Bob C. L. Li, 1974. "Insurance Exposure and Investment Risks: An Analysis Using Chance-Constrained Programming," Operations Research, INFORMS, vol. 22(5), pages 991-1007, October.
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    Cited by:

    1. Deza, Antoine & Huang, Kai & Metel, Michael R., 2015. "Chance constrained optimization for targeted Internet advertising," Omega, Elsevier, vol. 53(C), pages 90-96.
    2. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    3. Bismark Singh & Bernard Knueven, 2021. "Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system," Journal of Global Optimization, Springer, vol. 80(4), pages 965-989, August.
    4. Li, Susan X., 1998. "Stochastic models and variable returns to scales in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 104(3), pages 532-548, February.
    5. Limei Yan, 2009. "Optimal Programming Models for Portfolio Selection with Uncertain Chance Constraint," Modern Applied Science, Canadian Center of Science and Education, vol. 3(9), pages 1-84, September.
    6. Liu, Wenbin & Zhou, Zhongbao & Liu, Debin & Xiao, Helu, 2015. "Estimation of portfolio efficiency via DEA," Omega, Elsevier, vol. 52(C), pages 107-118.

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