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Optimal Allocation for Actuarial Liabilities: An Insurance Industry Application

In: Eurasian Business and Economics Perspectives

Author

Listed:
  • Débora M. Miranda

    (Sao Paulo School of Economics, FGV)

  • Alessandro M. Marques

    (Sao Paulo School of Economics, FGV)

Abstract

Insurance companies require solid risk management given its end activity. They are legally obligated to pay the claims described in the policies. They also need to have technical provisions to ensure the company’s solvency and sufficiency besides having enough resources to cover tailing events. To honor these commitments, they must manage their assets efficiently. We analyze an asset allocation strategy that guarantees the liability coverage required by various theoretical and one empirical disbursement structure. We use a cash flow matching model to ensure that asset-originated cash flows adequately cover the liability-driven outlay cash flows. The better the match between asset and liability, the lower the liquidity and market risk to which the company will be exposed. The model also gives the less expensive strategy of asset allocation and this way the surplus assets can be invested in risky assets in order to seek a higher rate of return for the portfolio. Empirical analysis uses an actual cash flow from a Brazilian insurance company focuses on the short-term automobile liability segment. Stress analysis of results confirms the model’s robustness. The obtained results bring a reflection not only about the asset allocation but also about the liability analysis which directly directs the allocation.

Suggested Citation

  • Débora M. Miranda & Alessandro M. Marques, 2024. "Optimal Allocation for Actuarial Liabilities: An Insurance Industry Application," Eurasian Studies in Business and Economics, in: Mehmet Hüseyin Bilgin & Hakan Danis & Ender Demir & Elcin Aykac Alp & Serkan Çankaya (ed.), Eurasian Business and Economics Perspectives, pages 407-437, Springer.
  • Handle: RePEc:spr:eurchp:978-3-031-51212-4_24
    DOI: 10.1007/978-3-031-51212-4_24
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