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Dynamic Portfolio Management for Property and Casualty Insurance

In: Stochastic Optimization Methods in Finance and Energy

Author

Listed:
  • Giorgio Consigli

    (University of Bergamo)

  • Massimo di Tria

    (Allianz Group, AIM)

  • Michele Gaffo

    (Allianz Investment Management, Allianz Group)

  • Gaetano Iaquinta

    (Weierstrass Institute for Applied Analysis and Stochastics)

  • Vittorio Moriggia

    (University of Bergamo)

  • Angelo Uristani

    (University of Bergamo)

Abstract

Recent trends in the insurance sector have highlighted the expansion of large insurance firms into asset management. In addition to their historical liability risk exposure associated with statutory activity, the growth of investment management divisions has caused increasing exposure to financial market fluctuations. This has led to stricter risk management requirements as reported in the Solvency II 2010 impact studies by the European Commission. The phenomenon has far-reaching implications for the definition of optimal asset–liability management (ALM) strategies at the aggregate level and for capital required by insurance companies. In this chapter we present an ALM model which combines in a dynamic framework an optimal strategic asset allocation problem for a large insurer and property and casualty (P&C) business constraints and tests it in a real-world case study. The problem is formulated as a multistage stochastic program (MSP) and the definition of the underlying uncertainty model, including financial as well as insurance risk factors, anticipates the model’s application under stressed liability scenarios. The benefits of a dynamic formulation and the opportunities arising from an integrated approach to investment and P&C insurance management are highlighted in this chapter.

Suggested Citation

  • Giorgio Consigli & Massimo di Tria & Michele Gaffo & Gaetano Iaquinta & Vittorio Moriggia & Angelo Uristani, 2011. "Dynamic Portfolio Management for Property and Casualty Insurance," International Series in Operations Research & Management Science, in: Marida Bertocchi & Giorgio Consigli & Michael A. H. Dempster (ed.), Stochastic Optimization Methods in Finance and Energy, edition 1, chapter 0, pages 99-124, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-9586-5_5
    DOI: 10.1007/978-1-4419-9586-5_5
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    Citations

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    Cited by:

    1. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    2. Sebastiano Vitali & Vittorio Moriggia, 2021. "Pension fund management with investment certificates and stochastic dominance," Annals of Operations Research, Springer, vol. 299(1), pages 273-292, April.
    3. Moriggia, Vittorio & Kopa, Miloš & Vitali, Sebastiano, 2019. "Pension fund management with hedging derivatives, stochastic dominance and nodal contamination," Omega, Elsevier, vol. 87(C), pages 127-141.
    4. Giorgio Consigli & Vittorio Moriggia & Sebastiano Vitali & Lorenzo Mercuri, 2018. "Optimal insurance portfolios risk-adjusted performance through dynamic stochastic programming," Computational Management Science, Springer, vol. 15(3), pages 599-632, October.
    5. Sebastiano Vitali & Ruth Domínguez & Vittorio Moriggia, 2021. "Comparing stage-scenario with nodal formulation for multistage stochastic problems," 4OR, Springer, vol. 19(4), pages 613-631, December.

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