Artikel
State space models and the KALMAN-filter in stochastic claims reserving: Forecasting, filtering and smoothing
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.
- Sprache
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Englisch
- Erschienen in
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 5 ; Year: 2017 ; Issue: 2 ; Pages: 1-44 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
state space models
KALMAN-filter
stochastic claims reserving
outstanding loss liabilities
ultimate loss
prediction uncertainty
chain ladder method
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chukhrova, Nataliya
Johannssen, Arne
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2017
- DOI
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doi:10.3390/risks5020030
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Chukhrova, Nataliya
- Johannssen, Arne
- MDPI
Entstanden
- 2017
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