Robustness in forecasting future liabilities in insurance

W.Y. Jessica Leung, S.T. Boris Choy

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

The Gaussian distribution has been widely used in statistical modelling.Being susceptible to outliers, the distribution hampers the robustness of statistical inference. In this paper, we propose two heavy-tailed distributions in the normal location-scale family and show that they are superior to the Gaussian distribution in the modelling of claim amount data from multiple lines of insurance business. Moreover, they also enable better forecasts of future liabilities and risk assessment and management. Implications on risk management practices are also discussed.

Original languageEnglish
Title of host publicationRobustness in Econometrics
EditorsVladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh
Place of PublicationCham Switzerland
PublisherSpringer
Pages187-200
Number of pages14
Edition1st
ISBN (Electronic)9783319507422
ISBN (Print)9783319844800, 9783319507415
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume692
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Keywords

  • Bayesian inference
  • Heavy-tailed distribution
  • Loss reserve
  • Markov chain Monte Carlo
  • Risk diversification

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