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Asset liability management for open pension schemes using multistage stochastic programming under Solvency-II-based regulatory constraints

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  • Duarte, Thiago B.
  • Valladão, Davi M.
  • Veiga, Álvaro

Abstract

Open private pension schemes are subject to risk-based regulation. In this context, asset and liability management (ALM) frameworks for pension plan operators are increasingly based on multistage stochastic programming (MSP). The significant advances in MSP modeling notwithstanding, previous works ignore risk-based regulatory constraints such as those in the Solvency II Directive. In this work, we propose an ALM model for open pension schemes based on an MSP model with a thorough representation of a risk-based regulation. Our proposal aims to define a dynamic optimal asset allocation including a detailed depiction of bond coupon payments, based on insolvency risk measures over a planning horizon. We present a realistic case study based on the Brazilian market, where the regulator imposes Solvency-II-compatible constraints on credit, underwriting, and operational risks. We develop a computationally tractable MSP model with explicit regulatory constraints, which induce risk aversion for even risk-neutral open pension plan operators.

Suggested Citation

  • Duarte, Thiago B. & Valladão, Davi M. & Veiga, Álvaro, 2017. "Asset liability management for open pension schemes using multistage stochastic programming under Solvency-II-based regulatory constraints," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 177-188.
    • Handle: RePEc:eee:insuma:v:77:y:2017:i:c:p:177-188
    DOI: 10.1016/j.insmatheco.2017.09.022
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    7. Roy Kouwenberg, 2018. "Strategic asset allocation for insurers under Solvency II," Journal of Asset Management, Palgrave Macmillan, vol. 19(7), pages 447-459, December.
    8. Chul Jang & Andrew Clare & Iqbal Owadally, 2024. "Liability-driven investment for pension funds: stochastic optimization with real assets," Risk Management, Palgrave Macmillan, vol. 26(3), pages 1-32, September.

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