@inbook{dc9171f9130949c584bd209b705e08d1,
title = "Robustness in forecasting future liabilities in insurance",
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.",
keywords = "Bayesian inference, Heavy-tailed distribution, Loss reserve, Markov chain Monte Carlo, Risk diversification",
author = "Leung, \{W.Y. Jessica\} and Choy, \{S.T. Boris\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.",
year = "2017",
doi = "10.1007/978-3-319-50742-2\_11",
language = "English",
isbn = "9783319844800",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "187--200",
editor = "Vladik Kreinovich and Songsak Sriboonchitta and Van-Nam Huynh",
booktitle = "Robustness in Econometrics",
address = "Switzerland",
edition = "1st",
}