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Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

Author

Listed:
  • Massimiliano Kaucic

    () (Department of Economics, Business, Mathematics and Statistics, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy)

  • Roberto Daris

    () (Department of Economics, Business, Mathematics and Statistics, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy)

Abstract

In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

Suggested Citation

  • Massimiliano Kaucic & Roberto Daris, 2015. "Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints," Risks, MDPI, Open Access Journal, vol. 3(3), pages 1-30, September.
  • Handle: RePEc:gam:jrisks:v:3:y:2015:i:3:p:390-419:d:55820
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    References listed on IDEAS

    as
    1. Trindade, A. Alexandre & Uryasev, Stan & Shapiro, Alexander & Zrazhevsky, Grigory, 2007. "Financial prediction with constrained tail risk," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3524-3538, November.
    2. Kaucic, Massimiliano, 2010. "Investment using evolutionary learning methods and technical rules," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1717-1727, December.
    3. Briec, Walter & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2013. "Portfolio selection with skewness: A comparison of methods and a generalized one fund result," European Journal of Operational Research, Elsevier, vol. 230(2), pages 412-421.
    4. Nunez-Letamendia, Laura, 2007. "Fitting the control parameters of a genetic algorithm: An application to technical trading systems design," European Journal of Operational Research, Elsevier, vol. 179(3), pages 847-868, June.
    5. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    6. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    7. Shukla, Pradyumn Kumar & Deb, Kalyanmoy, 2007. "On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1630-1652, September.
    8. Briec, Walter & Kerstens, Kristiaan, 2010. "Portfolio selection in multidimensional general and partial moment space," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 636-656, April.
    9. Catherine Bruneau & Sélim Mankai, 2012. "Optimal Economic Capital and Investment: Decisions for a Non-life Insurance Company," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00665516, HAL.
    10. Alejandro Balbas & Beatriz Balbas & Raquel Balbas, 2013. "Optimal Reinsurance: A Risk Sharing Approach," Risks, MDPI, Open Access Journal, vol. 1(2), pages 1-12, August.
    11. Jarraya, Bilel & Bouri, Abdelfettah, 2013. "Multiobjective optimization for the asset allocation of European nonlife insurance companies," MPRA Paper 53697, University Library of Munich, Germany, revised 2013.
    12. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, Open Access Journal, vol. 2(2), pages 1-14, April.
    13. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
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    More about this item

    Keywords

    multi-objective stochastic programming; performance indicators; chance constraint; normal constraint method; non-life insurance company;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting
    • K2 - Law and Economics - - Regulation and Business Law

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