Stochastic Claims Reserving Methods with State Space Representations: A Review

Nataliya Chukhrova and Arne Johannssen
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Nataliya Chukhrova: Faculty of Business Administration, University of Hamburg, 20146 Hamburg, Germany
Arne Johannssen: Faculty of Business Administration, University of Hamburg, 20146 Hamburg, Germany

Risks, 2021, vol. 9, issue 11, 1-55

Abstract: Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.

Keywords: adaptive learning; dependence modeling; evolutionary models; insurance; Kalman filter; machine learning; multivariate analysis; quantitative risk management; state space models; time series forecasting (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:9:y:2021:i:11:p:198-:d:672160

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