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Hidden semi-Markov models for rainfall-related insurance claims

Author

Listed:
  • Shi, Yue

    (Dept. of Business and Management Science, Norwegian School of Economics)

  • Punzo, Antonio

    (Dept. of Economics and Business, University of Catania)

  • Otneim, Håkon

    (Dept. of Business and Management Science, Norwegian School of Economics)

  • Maruotti, Antonello

    (Dept. GEPLI, LUMSA University)

Abstract

We analyze the temporal structure of a novel insurance dataset about home insurance claims related to rainfall-induced damage in Norway, and employ a hidden semi-Markov model to capture the non-Gaussian nature and temporal dynamics of these claims. By exploring a wide range of candidate distributions and evaluating their goodness-of-fit as well as commonly used risk measures, we identify a suitable model for effectively modeling insurance losses stemming from rainfall-related incidents. Our findings highlight the importance of considering the temporal aspects of weather-related insurance claims and demonstrate that the proposed hidden semi-Markov model adeptly captures this feature. Moreover, the model estimates reveal a concerning trend: the risks associated with heavy rain in the context of home insurance have exhibited an upward trajectory between 2004 and 2020, aligning with the evidence of a changing climate. This insight has significant implications for insurance companies, providing them with valuable information for accurate and robust modeling in the face of climate uncertainties. By shedding light on the evolving risks related to heavy rain and their impact on home insurance, our study offers essential insights for insurance companies to adapt their strategies and effectively manage these emerging challenges. It underscores the necessity of incorporating climate change considerations into insurance models and emphasizes the importance of continuously monitoring and reassessing risk levels associated with rainfall-induced damage. Ultimately, our research contributes to the broader understanding of climate risk in the insurance industry and supports the development of resilient and sustainable insurance practices.

Suggested Citation

  • Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    • Handle: RePEc:hhs:nhhfms:2023_017
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    References listed on IDEAS

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    More about this item

    Keywords

    Mixtures; Non-Gaussian distributions; EM algorithm; Risk measures; Rainfall data;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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