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A Row-Wise Stacking of the Runoff Triangle: State Space Alternatives for IBNR Reserve Prediction

Published online by Cambridge University Press:  09 August 2013

Rodrigo Atherino ,
Adrian Pizzinga  and
Cristiano Fernandes
Rodrigo Atherino
Affiliation:
JGP Global Gestao de Recursos, Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro
Cristiano Fernandes
Affiliation:
Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro
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Abstract

This work deals with prediction of IBNR reserve under a different data ordering of the non-cumulative runoff triangle. The rows of the triangle are stacked, resulting in a univariate time series with several missing values. Under this ordering, two approaches entirely based on state space models and the Kalman filter are developed, implemented with two real data sets, and compared with two well-established IBNR estimation methods — the chain ladder and an overdispersed Poisson regression model. The remarks from the empirical results are: (i) computational feasibility and efficiency; (ii) accuracy improvement for IBNR prediction; and (iii) flexibility regarding IBNR modeling possibilities.

Keywords

IBNRKalman filtermean square errormissing valuesstate space model
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 40 , Issue 2 , November 2010 , pp. 917 - 946
Copyright
Copyright © International Actuarial Association 2010

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