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<p>The estimation of risk for aggregated random variables is explored using (risk) measures within a broad and flexible class, called meanimiles. By separating the estimation of marginal distributions from the estimation of the dependence structure, modeled through copulas, this approach can avoid restrictive assumptions on the choice of risk measure, copula, or margins. The separation of dependence structure and margins allows for highly adaptable estimation procedures, accommodating parametric and nonparametric methods for both components. The framework’s generality enables its application to diverse contexts. Its utility is demonstrated in two real-data applications: optimizing horticultural auction portfolios and assessing disaster risk across US climate regions.</p>

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Copula-based estimation of meanimiles of aggregated risks

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Dieter Debrauwer,
  • Irène Gijbels

摘要

The estimation of risk for aggregated random variables is explored using (risk) measures within a broad and flexible class, called meanimiles. By separating the estimation of marginal distributions from the estimation of the dependence structure, modeled through copulas, this approach can avoid restrictive assumptions on the choice of risk measure, copula, or margins. The separation of dependence structure and margins allows for highly adaptable estimation procedures, accommodating parametric and nonparametric methods for both components. The framework’s generality enables its application to diverse contexts. Its utility is demonstrated in two real-data applications: optimizing horticultural auction portfolios and assessing disaster risk across US climate regions.