Journal: Insurance: Mathematics and Economics

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Elsevier

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ISSN

0167-6687
1873-5959

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Publications 1 - 10 of 27
  • Optimal reinsurance from an optimal transport perspective
    Item type: Journal Article
    Acciaio, Beatrice; Albrecher, Hansjörg; García Flores, Brandon (2025)
    Insurance: Mathematics and Economics
    We use the randomization idea and proof techniques from optimal transport to study optimal reinsurance problems. We start by providing conditions for a class of problems that allow us to characterize the support of optimal treaties, and show how this can be used to deduce the shape of the optimal contract, reducing the task to an optimization problem with finitely many constraints, for which standard techniques can be applied. For a more general class of problems, we regard the optimal reinsurance problem as an iterated optimal transport problem between a (known) initial risk exposure of the insurer and an (unknown) resulting risk exposure of the reinsurer. The proposed approach provides a general framework that encompasses many reinsurance problems, which we illustrate in several concrete examples, providing alternative proofs to classical optimal reinsurance results, as well as establishing new optimality results, some of which contain optimal treaties that involve external randomness.
  • Worst VaR scenarios
    Item type: Conference Paper
    Embrechts, Paul; Höing, Andrea; Puccetti, Giovanni (2005)
    Insurance: Mathematics and Economics
  • Prediction error in the chain ladder method
    Item type: Journal Article
    Wüthrich, Mario V. (2008)
    Insurance: Mathematics and Economics
  • Optimal surrender policy for variable annuity guarantees
    Item type: Journal Article
    Bernard, Carole; MacKay, Anne; Muehlbeyer, Max (2014)
    Insurance: Mathematics and Economics
  • Risk exchange under infinite-mean Pareto models
    Item type: Journal Article
    Chen, Yuyu; Embrechts, Paul; Wang, Ruodu (2025)
    Insurance: Mathematics and Economics
    We study the optimal decisions and equilibria of agents who aim to minimize their risks by allocating their positions over extremely heavy-tailed (i.e., infinite-mean) and possibly dependent losses. The loss distributions of our focus are super-Pareto distributions, which include the class of extremely heavy-tailed Pareto distributions. Using a recent result on stochastic dominance, we show that for a portfolio of super-Pareto losses, non-diversification is preferred by decision makers equipped with well-defined and monotone risk measures. The phenomenon that diversification is not beneficial in the presence of super-Pareto losses is further illustrated by an equilibrium analysis in a risk exchange market. First, agents with super-Pareto losses will not share risks in a market equilibrium. Second, transferring losses from agents bearing super-Pareto losses to external parties without any losses may arrive at an equilibrium which benefits every party involved.
  • Paid-incurred chain claims reserving method
    Item type: Journal Article
    Merz, Michael; Wüthrich, Mario V. (2010)
    Insurance: Mathematics and Economics
  • Copula based hierarchical risk aggregation through sample reordering
    Item type: Journal Article
    Arbenz, Philipp; Hummel, Christoph; Mainik, Georg (2012)
    Insurance: Mathematics and Economics
  • On the lifetime and one-year views of reserve risk, with application to IFRS 17 and Solvency II risk margins
    Item type: Journal Article
    England, Peter D.; Verrall, Richard J.; Wüthrich, Mario V. (2019)
    Insurance: Mathematics and Economics
  • What can we learn from telematics car driving data: A survey
    Item type: Journal Article
    Gao, Guangyuan; Meng, Shengwang; Wüthrich, Mario V. (2022)
    Insurance: Mathematics and Economics
    We give a survey on the field of telematics car driving data research in actuarial science. We describe and discuss telematics car driving data, we illustrate the difficulties of telematics data cleaning, and we highlight the transparency issue of telematics car driving data resulting in associated privacy concerns. Transparency of telematics data is demonstrated by aiming at correctly allocating different car driving trips to the right drivers. This is achieved rather successfully by a convolutional neural network that manages to discriminate different car drivers by their driving styles. In a last step, we describe two approaches of using telematics data for improving claims frequency prediction, one is based on telematics heatmaps and the other one on time series of individual trips, respectively.
  • Capital requirements with defaultable securities
    Item type: Journal Article
    Farkas, Walter; Koch-Medina, Pablo; Munari, Cosimo (2014)
    Insurance: Mathematics and Economics
Publications 1 - 10 of 27