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Astin Bulletin is a peer-reviewed journal published by Cambridge University Press, covering Insurance, Mortality, Demography, Risk Management, Insurance and Financial Risk Management, Probability and Risk Models.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Astin Bulletin 12 (2023) 45–58.
Formats any DOI in the closest standard style — Astin Bulletin has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Insurance, Mortality, Demography, Risk Management Insurance and Financial Risk Management Probability and Risk Models Diverse Scientific and Economic Studies Legal case studies and regulations |
| ISSN | 0515-0361 |
| Citation impact (2-yr) | 1.62 |
| h-index | 85 |
| i10-index | 747 |
| Total citations | 35,346 |
| Top institutions publishing here | University of Waterloo |
| Journal website | journals.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This paper examines a class of premium functionals which are (i) comonotonic additive and (ii) stochastic dominance preservative. The representation for this class is a transformation of the decumulative distribution function. It has close connections with the recent developments in economic decision theory and non-additive measure theory. Among a few elementary members of this…
Compound distributions such as the compound Poisson and the compound negative binomial are used extensively in the theory of risk to model the distribution of the total claims incurred in a fixed period of time. The usual method of evaluating the distribution function requires the computation of many convolutions of the conditional distribution of the…
Abstract A distribution-free formula for the standard error of chain ladder reserve estimates is derived and compared to the results of some parametric methods using a numerical example.
Abstract Good estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive…
Classical statistics deals with the following standard problem of estimation: Given : random variables X 1 , X 2 … X n independent, identically distributed, and observations x 1 , X 2 … x n , Estimate : parameter (or function thereof) of the distribution function common to all X i . It is not…
