Optimal insurance contracts under distortion risk measures with ambiguity aversion

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      <subfield code="a">Jiang, Wenjun </subfield>
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      <subfield code="a">Optimal insurance contracts under distortion risk measures with ambiguity aversion</subfield>
      <subfield code="c">Wenjun Jiang, Marcos Escobar-Anel, Jiandong Ren</subfield>
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      <subfield code="a">This paper presents analytical representations for an optimal insurance contract under distortion risk measure and in the presence of model uncertainty. We incorporate ambiguity aversion and distortion risk measure through the model of Robert and Therond. Explicit optimal insurance indemnity functions are derived when the decision maker (DM) applies Value-at-Risk as risk measure and is ambiguous about the loss distribution. Our results show that: (1) undermodel uncertainty, ambiguity aversion results in a distorted probability distribution over the set of possible models with a bias in favor of the model which yields a larger risk; (2) a more ambiguity-averse DM would demand more insurance coverage; (3) for a given budget, uncertainties about the loss distribution result in higher risk level for the DM.</subfield>
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      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
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      <subfield code="g">01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 619-646</subfield>
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