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Evaluation of insurance products with guarantee in incomplete markets

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  • Consiglio, Andrea
  • De Giovanni, Domenico

Abstract

Life insurance products are usually equipped with minimum guarantee and bonus provision options. The pricing of such claims is of vital importance for the insurance industry. Risk management, strategic asset allocation, and product design depend on the correct evaluation of the written options. Also regulators are interested in such issues since they have to be aware of the possible scenarios that the overall industry will face. Pricing techniques based on the Black & Scholes paradigm are often used, however, the hypotheses underneath this model are rarely met. To overcome Black & Scholes limitations, we develop a stochastic programming model to determine the fair price of the minimum guarantee and bonus provision options. We show that such a model covers the most relevant sources of incompleteness accounted in the financial and insurance literature. We provide extensive empirical analyses to highlight the effect of incompleteness on the fair value of the option, and show how the whole framework can be used as a valuable normative tool for insurance companies and regulators.

Suggested Citation

  • Consiglio, Andrea & De Giovanni, Domenico, 2008. "Evaluation of insurance products with guarantee in incomplete markets," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 332-342, February.
    • Handle: RePEc:eee:insuma:v:42:y:2008:i:1:p:332-342
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    References listed on IDEAS

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    Cited by:

    1. Ivan Peñaloza & Pablo Padilla, 2022. "A Pricing Method in a Constrained Market with Differential Informational Frameworks," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1055-1100, October.
    2. Consiglio, Andrea & Carollo, Angelo & Zenios, Stavros A., 2014. "Generating Multi-factor Arbitrage-Free Scenario Trees with Global Optimization," Working Papers 13-35, University of Pennsylvania, Wharton School, Weiss Center.
    3. Gan, Guojun, 2013. "Application of data clustering and machine learning in variable annuity valuation," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 795-801.
    4. Andrea Consiglio & Domenico De Giovanni, 2010. "Pricing the Option to Surrender in Incomplete Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(4), pages 935-957, December.
    5. Barro, Diana & Consigli, Giorgio & Varun, Vivek, 2022. "A stochastic programming model for dynamic portfolio management with financial derivatives," Journal of Banking & Finance, Elsevier, vol. 140(C).

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