Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving

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 6, 1-5

Abstract: In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction. Although state space models and the associated Kalman filter learning algorithms are very powerful and flexible tools, comparatively few articles on this topic were published during this period. Most recently, several articles have been published which highlight the benefits of state space models in stochastic claims reserving and may lead to a significant increase in its popularity for applications in actuarial practice. To further emphasize the merits of these papers, this commentary highlights various additional aspects that are useful for practical applications and offer some fruitful directions for future research.

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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