Parameter Reduction in Actuarial Triangle Models

29 Pages Posted: 26 Jun 2017 Last revised: 8 Jan 2018

See all articles by Gary Venter

Gary Venter

Columbia University

Roman Gutkovich

Independent

Qian Gao

AIG International, Inc.

Date Written: January 19, 2017

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 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 as there are too many parameters to fit reliably, but usually this is an ad hoc exercise. Here we try two formal statistical approaches to parameter reduction, random effects and Lasso, and discuss methods of comparing goodness of fit.

Suggested Citation

Venter, Gary and Gutkovich, Roman and Gao, Qian, Parameter Reduction in Actuarial Triangle Models (January 19, 2017). Variance, Forthcoming; UNSW Business School Research Paper Forthcoming. Available at SSRN: https://ssrn.com/abstract=2992300 or http://dx.doi.org/10.2139/ssrn.2992300

Gary Venter (Contact Author)

Columbia University ( email )

116th and Broadway
New York, NY 10027
United States

Roman Gutkovich

Independent

No Address Available

Qian Gao

AIG International, Inc. ( email )

Greenwich, CT
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
47
Abstract Views
369
PlumX Metrics