On unbalanced data and common shock models in stochastic loss reserving

Benjamin Avanzi; Gregory Clive Taylor; Anh Vu Phuong; Bernard Wong

Journal article

On unbalanced data and common shock models in stochastic loss reserving

Benjamin Avanzi, Gregory Clive Taylor, Anh Vu Phuong, Bernard Wong

Annals of Actuarial Science | Cambridge University Press (CUP) | Published : 2021

DOI: 10.1017/S1748499520000196

Abstract

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful ..

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University of Melbourne Researchers

Benjamin Avanzi Author

Related Projects (1)

Modelling claim dependencies for the general insurance industry with economic capital in view: An innovative approach with stochastic processes

Grants

Awarded by Australian Research Council

Funding Acknowledgements

Results in this paper were presented at The Australasian Actuarial Education and Research Symposium in 2017 and the 22nd International Congress on Insurance: Mathematics and Economics in 2018. The authors are grateful for constructive comments received from colleagues who attended these conferences. The authors are also thankful to the two anonymous reviewers for their constructive comments that helped significantly improve the paper. 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, Phuong Anh Vu acknowledges financial support from a University International Postgraduate Award/University Postgraduate Award and supplementary scholarships provided by the UNSW Business School. The views expressed herein are those of the authors and are not necessarily those of the supporting organisations.