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Analyzing Size of Loss Frequency Distribution Patterns: Uncovering the Impact of the COVID-19 Pandemic

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  • Shengkun Xie

    (Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

  • Yuanshun Li

    (School of Accounting and Finance, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

Abstract

This study delves into a critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviours and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions.

Suggested Citation

  • Shengkun Xie & Yuanshun Li, 2024. "Analyzing Size of Loss Frequency Distribution Patterns: Uncovering the Impact of the COVID-19 Pandemic," Risks, MDPI, vol. 12(2), pages 1-19, February.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:2:p:40-:d:1340869
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    References listed on IDEAS

    as
    1. Azimli, Asil, 2020. "The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach," Finance Research Letters, Elsevier, vol. 36(C).
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