Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195886 
Year of Publication: 
2017
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 5 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-44
Publisher: 
MDPI, Basel
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.
Subjects: 
state space models
KALMAN-filter
stochastic claims reserving
outstanding loss liabilities
ultimate loss
prediction uncertainty
chain ladder method
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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