Claims Reserving with a Stochastic Vector Projection

Portugal, Luis, Pantelous, Athanasios A ORCID: 0000-0001-5738-1471 and Assa, Hirbod (2018) Claims Reserving with a Stochastic Vector Projection. NORTH AMERICAN ACTUARIAL JOURNAL, 22 (1). pp. 22-39.

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Abstract

In the last three decades, a variety of stochastic reserving models have been proposed in the general insurance literature mainly using (or reproducing) the well-known Chain-Ladder claims-reserving estimates. In practice, when the data do not satisfy the Chain-Ladder assumptions, high prediction errors might occur. Thus, in this article, a combined methodology is proposed based on the stochastic vector projection method and uses the regression through the origin approach of Murphy, but with heteroscedastic errors instead, and different from those that used by Mack. Furthermore, the Mack distribution-free model appears to have higher prediction errors when compared with the proposed one, particularly, for data sets with increasing (regular) trends. Finally, three empirical examples with irregular and regular data sets illustrate the theoretical findings, and the concepts of best estimate and risk margin are reported.

Item Type:	Article
Additional Information:	Source info: North American Actuarial Journal, Volume 22, Issue 1, pp. 22-39, March 2018, DOI 10.1080/10920277.2017.1353429
Uncontrolled Keywords:	Stochastic Reserving, Chain-Ladder Distribution-Free, Vector Projection, Best Estimate, Risk Margin, Link Ratios, Loss Development Factors, Homoscedastic and Heteroscedastic Errors, Prediction Errors
Depositing User:	Symplectic Admin
Date Deposited:	10 Feb 2017 10:15
Last Modified:	19 Jan 2023 07:19
DOI:	10.1080/10920277.2017.1353429
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3005704