On evaluation of joint risk for nonnegative multivariate risks under dependence uncertainty

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      <subfield code="a">The article develops a novel axiomatic approach to the evaluation of joint risk in multivariate vectors of non-negative risks under dependence uncertainty. It introduces distortion-based joint risk measures, both scalar and vector-valued, and analyses their fundamental properties. The work explicitly addresses model uncertainty through model-free frameworks and the use of copulas. In addition, it establishes formal connections with existing multivariate risk measures such as Value at Risk (VaR), Conditional Tail Expectation (CTE), and tail-based risk measures. The results provide a solid theoretical foundation for advanced risk management in insurance and finance</subfield>
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      <subfield code="g">20/04/2026 Volumen 56 Número 2 - abril 2026 , 25 p.</subfield>
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