Author:
Chukhrova Nataliya,Johannssen Arne
Abstract
This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL) method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.
Subject
Strategy and Management,Economics, Econometrics and Finance (miscellaneous),Accounting
Reference34 articles.
1. A state space model for rub-off triangles
2. A row-wise Stacking of the Runoff Triangle: State Space Alternatives for IBNR Reserve Prediction;Atherino;ASTIN Bulletin,2010
3. Time Series: Theory and Methods;Brockwell,2006
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献