Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-06-05T14:28:18.125Z Has data issue: false hasContentIssue false

Auto-balanced common shock claim models

Published online by Cambridge University Press:  12 April 2023

Greg Taylor*
Affiliation:
School of Risk and Actuarial Studies, University of New South Wales, UNSW Sydney, NSW 2052, Australia
Phuong Anh Vu
Affiliation:
Taylor Fry, 45 Clarence Street, Sydney, NSW 2000, Australia
*
*Corresponding author. E-mail: greg_taylor60@hotmail.com

Abstract

The paper is concerned with common shock models of claim triangles. These are usually constructed as linear combinations of shock components and idiosyncratic components. Previous literature has discussed the unbalanced property of such models, whereby the shocks may over- or under-contribute to some observations. The literature has also introduced corrections for this. The present paper discusses “auto-balanced” models, in which all shock and idiosyncratic components contribute to observations such that their proportionate contributions are constant from one observation to another. The conditions for auto-balance are found to be simple and applicable to a wide range of model structures. Numerical illustrations are given.

Type
Original Research Paper
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alai, D.H., Landsman, Z. & Sherris, M. (2013). Lifetime dependence modelling using a truncated multivariate gamma distribution. Insurance: Mathematics and Economics, 52(3), 542549.Google Scholar
Alai, D.H., Landsman, Z. & Sherris,, M. (2016). Multivariate Tweedie lifetimes: the impact of dependence. Scandinavian Actuarial Journal, 2016(8), 692712.10.1080/03461238.2015.1007891CrossRefGoogle Scholar
Avanzi, B., Taylor, G., Vu, P.A. & Wong, B. (2016). Stochastic loss reserving with dependence: a flexible multivariate Tweedie approach. Insurance: Mathematics and Economics, 71, 6378.Google Scholar
Avanzi, B., Taylor, G., Vu, P.A. & Wong, B. (2021). On unbalanced data and common shock models in stochastic loss reserving. Annals of Actuarial Science, 15(1), 173203 10.1017/S1748499520000196CrossRefGoogle Scholar
Avanzi, B., Taylor, G. & Wong, B. (2018). Common shock models for claim arrays. ASTIN Bulletin, 48(3), 128.10.1017/asb.2018.18CrossRefGoogle Scholar
Furman, E. & Landsman, Z. (2010). Multivariate Tweedie distributions and some related capital-at-risk analyses. Insurance: Mathematics and Economics, 46, 351361.Google Scholar
Jørgensen, B. (1987). Exponential dispersion models. Journal of the Royal Statistical Society, Series B, 49(2), 127162.Google Scholar
Jørgensen, B. (1997). The Theory of Dispersion Models. Chapman & Hall, London, UK.Google Scholar
Lindskog, F. & McNeil, A.J. (2003). Common Poisson shock models: applications to insurance and credit risk modelling. ASTIN Bulletin, 33(2), 209238.CrossRefGoogle Scholar
Meyers, G.G. (2007). The common shock model for correlated insurance losses. Variance, 1(1), 4052.Google Scholar
Shi, P., Basu, S. & Meyers, G.G. (2012). A Bayesian log-normal model for multivariate loss reserving. North American Actuarial Journal, 16(1), 2951.10.1080/10920277.2012.10590631CrossRefGoogle Scholar