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Machine Learning in Nested Simulations Under Actuarial Uncertainty

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Gilberto Castellani

    (Sapienza University of Rome)

  • Ugo Fiore

    (University of Naples Parthenope)

  • Zelda Marino

    (University of Naples Parthenope)

  • Luca Passalacqua

    (Sapienza University of Rome)

  • Francesca Perla

    (University of Naples Parthenope)

  • Salvatore Scognamiglio

    (University of Naples Parthenope)

  • Paolo Zanetti

    (University of Naples Parthenope)

Abstract

The Solvency II directive states that in order to be solvent the insurance undertakings must to hold eligible own funds covering the Solvency Capital Requirement (SCR), which is defined as the Value-at-Risk of the NAV probability distribution (PDF in the directive) at a confidence level of 99.5% over a one-year period. The estimation of the SCR requires the evaluation of the NAV (under risk-neutral probabilities) conditionally to the economic and actuarial scenarios estimated under real-world probabilities and involve nested Monte Carlo simulations. This approach usually presents unacceptable computational costs. In this paper we analyse the performance of Machine Learning techniques on some insurance portfolios considering a multivariate stochastic model for actuarial risks including mortality, lapse and expense risks. Experiments are aimed not only to analyse the performance of these techniques in a large-dimensional risk framework, but also to investigate variability and robustness of the obtained estimations.

Suggested Citation

  • Gilberto Castellani & Ugo Fiore & Zelda Marino & Luca Passalacqua & Francesca Perla & Salvatore Scognamiglio & Paolo Zanetti, 2021. "Machine Learning in Nested Simulations Under Actuarial Uncertainty," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 137-143, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_21
    DOI: 10.1007/978-3-030-78965-7_21
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