Parameter Reduction in Actuarial Triangle Models

By Gary G. Venter, Roman Gutkovich, Qian Gao

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Abstract

Very similar modeling is done for actuarial models in loss reserving and mortality projection. Both start with incomplete data rectangles, traditionally called triangles, and model the data by year of origin, year of observation, and lag from origin to observation. Actuaries using these models almost always use some form of parameter reduction because there are too many parameters to fit reliably, but usually such adjustment is an ad hoc exercise. In this paper, we try two formal statistical approaches to parameter reduction, random effects and LASSO (least absolute shrinkage and selection operator), and discuss methods of comparing goodness of fit.

Keywords: Random effects, loss reserving, mortality, joint dataset modeling.

Citation

Venter, Gary G., Roman Gutkovich, and Qian Gao, "Parameter Reduction in Actuarial Triangle Models," Variance 12:2, 2019, pp. 142-160.

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Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.