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Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets

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  • Gan Guojun

    (Department of Mathematics, University of Connecticut, Storrs, Connecticut 06269, USA)

  • Valdez Emiliano A.

    (Department of Mathematics, University of Connecticut, Storrs, Connecticut 06269, USA)

Abstract

Metamodeling techniques have recently been proposed to address the computational issues related to the valuation of large portfolios of variable annuity contracts. However, it is extremely diffcult, if not impossible, for researchers to obtain real datasets frominsurance companies in order to test their metamodeling techniques on such real datasets and publish the results in academic journals. To facilitate the development and dissemination of research related to the effcient valuation of large variable annuity portfolios, this paper creates a large synthetic portfolio of variable annuity contracts based on the properties of real portfolios of variable annuities and implements a simple Monte Carlo simulation engine for valuing the synthetic portfolio. In addition, this paper presents fair market values and Greeks for the synthetic portfolio of variable annuity contracts that are important quantities for managing the financial risks associated with variable annuities. The resulting datasets can be used by researchers to test and compare the performance of various metamodeling techniques.

Suggested Citation

  • Gan Guojun & Valdez Emiliano A., 2017. "Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets," Dependence Modeling, De Gruyter, vol. 5(1), pages 354-374, December.
    • Handle: RePEc:vrs:demode:v:5:y:2017:i:1:p:354-374:n:21
    DOI: 10.1515/demo-2017-0021
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    References listed on IDEAS

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    2. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.

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