Journal: ASTIN Bulletin

Abbreviation

ASTIN bull.

Publisher

Cambridge University Press

Journal Volumes

ISSN

0515-0361
1783-1350

Description

Filters

Reset filters

Search Results

Publications 1 - 10 of 36
  • Pareto Optimal Risk Exchanges and a System of Differential Equations: a Duality Theorem
    Item type: Journal Article
    Wyler, Erich (1990)
    ASTIN Bulletin
  • An Economic Premium Principle
    Item type: Journal Article
    Hans Bühlmann, (1980)
    ASTIN Bulletin
  • Discrimination-Free Insurance Pricing
    Item type: Journal Article
    Lindholm, Mathias; Richman, Ronald; Tsanakas, Andreas; et al. (2022)
    ASTIN Bulletin
    We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.
  • Modeling and Generating Dependent Risk Processes for IRM and DFA
    Item type: Journal Article
    Pfeifer, Dietmar; Nešlehová, Johanna (2004)
    ASTIN Bulletin
    Modern Integrated Risk Management (IRM) and Dynamic Financial Analysis (DFA) rely in great part on an appropriate modeling of the stochastic behavior of the various risky assets and processes that influence the performance of the company under consideration. A major challenge here is a more substantial and realistic description and modeling of the various complex dependence structures between such risks showing up on all scales. In this presentation, we propose some approaches towards modeling and generating (simulating) dependent risk processes in the framework of collective risk theory, in particular w.r.t. dependent claim number processes of Poisson type (homogeneous and non-homogeneous), and compound Poisson processes.
  • A Neural Network Boosted Double Overdispersed Poisson Claims Reserving Model
    Item type: Journal Article
    Gabrielli, Andrea (2020)
    ASTIN Bulletin
  • Credibility Approximations for Bayesian Prediction of Second Moments
    Item type: Journal Article
    Jewell, William S.; Schnieper, Rene (1985)
    ASTIN Bulletin
    Credibility theory refers to the use of linear least-squares theory to approximate the Bayesian forecast of the mean of a future observation; families are known where the credibility formula is exact Bayesian. Second-moment forecasts are also of interest, for example, in assessing the precision of the mean estimate. For some of these same families, the second-moment forecast is exact in linear and quadratic functions of the sample mean. On the other hand, for the normal distribution with normal-gamma prior on the mean and variance, the exact forecast of the variance is a linear function of the sample variance and the squared deviation of the sample mean from the prior mean. Bühlmann has given a credibility approximation to the variance in terms of the sample mean and sample variance. In this paper, we present a unified approach to estimating both first and second moments of future observations using linear functions of the sample mean and two sample second moments; the resulting least-squares analysis requires the solution of a 3 × 3 linear system, using 11 prior moments from the collective and giving joint predictions of all moments of interest. Previously developed special cases follow immediately. For many analytic models of interest, 3-dimensional joint prediction is significantly better than independent forecasts using the “natural” statistics for each moment when the number of samples is small. However, the expected squared-errors of the forecasts become comparable as the sample size increases.
  • STATISTICAL INFERENCE FOR COPULAS IN HIGH DIMENSIONS
    Item type: Journal Article
    Embrechts, Paul; Hofert, Marius (2013)
    ASTIN Bulletin
  • Comment on the Discussion Article by Aase and Persson
    Item type: Journal Article
    Bühlmann, Hans (2003)
    ASTIN Bulletin
  • The History of ASTIN. Invited Lecture at the 50 Years Anniversary of ASTIN
    Item type: Journal Article
    Bühlmann, Hans (2007)
    ASTIN Bulletin
Publications 1 - 10 of 36