Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/145669
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- Title
- Semiparametric model for prediction of individual claim loss reserving
- Related
- Insurance, mathematics and economics, Vol. 45, No. 1, (2009), p.1-8
- DOI
- 10.1016/j.insmatheco.2009.02.009
- Publisher
- Elsevier BV
- Date
- 2009
- FoR/RFCD Code(s)
-
010400 Statistics
140200 Applied Economics
- Author/Creator
- Zhao, Xiao Bing
- Author/Creator
- Zhou, Xian
- Author/Creator
- Wang, Jing Long
- Description
- The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Conventional methods, such as ladder or separation methods based on aggregated or grouped claims of the so-called “run-off triangle”, have been illustrated to have some drawbacks. Recently, individual claim loss models have attracted a great deal of interest in actuarial literature, which can overcome the shortcomings of aggregated claim loss models. In this paper, we propose an alternative individual claim loss model, which has a semiparametric structure and can be used to fit flexibly the claim loss reserving. Local likelihood is employed to estimate the parametric and nonparametric components of the model, and their asymptotic properties are discussed. Then the prediction of the IBNR claim loss reserving is investigated. A simulation study is carried out to evaluate the performance of the proposed methods.
- Description
- 8 page(s)
- Subject Keyword
- 010400 Statistics
- Subject Keyword
- 140200 Applied Economics
- Subject Keyword
- IBNR claim
- Subject Keyword
- loss reserving
- Subject Keyword
- aggregated claim model
- Subject Keyword
- individual claim loss model
- Subject Keyword
- semiparametric structure
- Subject Keyword
- local likelihood
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Actuarial Studies
- Identifier
- http://hdl.handle.net/1959.14/145669
- Identifier
- ISSN:0167-6687
- Identifier
- mq-rm-2009000638
- Language
- eng
- Reviewed
