On a new paradigm of optimal reinsurance : a stochastic stackelberg differential game between an insurer and a reinsurer

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      <subfield code="a">Chen, Lv</subfield>
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      <subfield code="a">On a new paradigm of optimal reinsurance</subfield>
      <subfield code="b"> : a stochastic stackelberg differential game between an insurer and a reinsurer</subfield>
      <subfield code="c">Lv Chen, Yang Shen</subfield>
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      <subfield code="a">This paper proposes a new continuous-time framework to analyze optimal reinsurance, in which an insurer and a reinsurer are two players of a stochastic Stackelberg differential game, i.e., a stochastic leader-follower differential game. This allows us to determine optimal reinsurance from joint interests of the insurer and the reinsurer, which is rarely considered in the continuous-time setting. In the Stackelberg game, the reinsurer moves first and the insurer does subsequently to achieve a Stackelberg equilibrium toward optimal reinsurance arrangement. Speaking more precisely, the reinsurer is the leader of the game and decides on an optimal reinsurance premium to charge, while the insurer is the follower of the game and chooses an optimal proportional reinsurance to purchase. Under utility maximization criteria, we study the game problem starting from the general setting with generic utilities and random coefficients to the special case with exponential utilities and constant coefficients. In the special case, we find that the reinsurer applies the variance premium principle to calculate the optimal reinsurance premium and the insurer's optimal ceding/retained proportion of insurance risk depends not only on the risk aversion of itself but also on that of the reinsurer</subfield>
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      <subfield code="a">Matemática del seguro</subfield>
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      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
      <subfield code="x">0515-0361</subfield>
      <subfield code="g">01/05/2018 Volumen 48 Número 2 - mayo 2018 , p. 905-960</subfield>
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