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Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants

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  • Gribkova, N.V.
  • Su, J.
  • Zitikis, R.

Abstract

We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results.

Suggested Citation

  • Gribkova, N.V. & Su, J. & Zitikis, R., 2022. "Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 199-222.
    • Handle: RePEc:eee:insuma:v:107:y:2022:i:c:p:199-222
    DOI: 10.1016/j.insmatheco.2022.08.009
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    References listed on IDEAS

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    Cited by:

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    2. Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).

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    More about this item

    Keywords

    Capital allocations; Marginal expected shortfall; Compound sums; Order statistics; Concomitants;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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