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A Better Trigger: Indices for Insurance

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  • Tse‐Ling Teh
  • Christopher Woolnough

Abstract

Index triggers have enabled the extension of insurance to a wide range of risks, by providing a simple mechanism to determine payment. However, the resulting coverage generates basis risk, the variability over time in the level of insurance payouts relative to the level of losses. We analyze basis risk to rank binary and multivalue indices for any risk‐averse individual. Our ranking provides methods to select an index that is optimal for a heterogeneous group and illustrates that higher correlation between loss and index, does not necessarily equate to a better index.

Suggested Citation

  • Tse‐Ling Teh & Christopher Woolnough, 2019. "A Better Trigger: Indices for Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 86(4), pages 861-885, December.
    • Handle: RePEc:bla:jrinsu:v:86:y:2019:i:4:p:861-885
    DOI: 10.1111/jori.12242
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    Cited by:

    1. Robert Hartwig & Greg Niehaus & Joseph Qiu, 2020. "Insurance for economic losses caused by pandemics," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 45(2), pages 134-170, September.
    2. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    3. Zhixia Wu & Xiazhong Zheng & Yijun Chen & Shan Huang & Wenli Hu & Chenfei Duan, 2023. "Urban Flood Loss Assessment and Index Insurance Compensation Estimation by Integrating Remote Sensing and Rainfall Multi-Source Data: A Case Study of the 2021 Henan Rainstorm," Sustainability, MDPI, vol. 15(15), pages 1-18, July.

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