Journal article
On the impact of outliers in loss reserving
B Avanzi, M Lavender, G Taylor, B Wong
European Actuarial Journal | SPRINGER HEIDELBERG | Published : 2024
Abstract
The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. It has been shown that the popular chain-ladder reserving approach is at significant risk to such aberrant observations in that reserve estimates can be significantly shifted in the presence of even one outlier. As a consequence the chain-ladder reserving technique is non-robust. In this paper we investigate the sensitivity of reserves and mean squared errors of prediction under Mack’s Model (ASTIN Bull 23(2):213–225, 1993). This is done through the derivation of impact functions which are calculated by taking the first derivative of the relevant statistic of i..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
Earlier versions of this paper were presented at the Australian Actuaries Institute General Insurance Seminar conference in Melbourne (Australia), as well as at an Australian Actuaries Institute Insights session in Sydney (Australia). The authors are grateful for constructive comments received from colleagues who attended this event and from those who read the earlier version of the paper published online to support the presentation. The authors are also grateful to Sharanjit Paddam and Will Turvey for comments on earlier drafts of the paper, as well as William Cheng, Wanzhang (Simon) Jing, and Yun Wai (William) Ho for their research assistance. This research was supported under Australian Research Council's Linkage (LP130100723, with funding partners Allianz Australia Insurance Ltd, Insurance Australia Group Ltd, and Suncorp Metway Ltd) and Discovery (DP200101859) Projects funding schemes. Furthermore, Lavender acknowledges support from the UNSW Business School Honours Scholarship. The views expressed herein are those of the authors and are not necessarily those of the supporting organisations. Open Access funding enabled and organized by CAUL and its Member Institutions.