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Risk analysis with categorical explanatory variables

Author

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  • Kang, Seul Ki
  • Peng, Liang
  • Xiao, Hongmin

Abstract

To better forecast the Value-at-Risk of the aggregate insurance losses, Heras et al. (2018) propose a two-step inference of using logistic regression and quantile regression without providing detailed model assumptions, deriving the related asymptotic properties, and quantifying the inference uncertainty. This paper argues that the application of quantile regression at the second step is not necessary when explanatory variables are categorical. After describing the explicit model assumptions, we propose another two-step inference of using logistic regression and the sample quantile. Also, we provide an efficient empirical likelihood method to quantify the uncertainty. A simulation study confirms the good finite sample performance of the proposed method.

Suggested Citation

  • Kang, Seul Ki & Peng, Liang & Xiao, Hongmin, 2020. "Risk analysis with categorical explanatory variables," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 238-243.
    • Handle: RePEc:eee:insuma:v:91:y:2020:i:c:p:238-243
    DOI: 10.1016/j.insmatheco.2020.02.007
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    References listed on IDEAS

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    2. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149.
    3. Kudryavtsev, Andrey A., 2009. "Using quantile regression for rate-making," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 296-304, October.
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    Cited by:

    1. Gao, Suhao & Yu, Zhen, 2023. "Parametric expectile regression and its application for premium calculation," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 242-256.

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