Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/9/6/112 |
| _version_ | 1797531156643577856 |
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| author | Nataliya Chukhrova Arne Johannssen |
| author_facet | Nataliya Chukhrova Arne Johannssen |
| author_sort | Nataliya Chukhrova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:39:56Z |
| format | Article |
| id | doaj.art-557eec4ca5fa483a94c6f9fe7170ea8b |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-10T10:39:56Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-557eec4ca5fa483a94c6f9fe7170ea8b2023-11-21T23:00:49ZengMDPI AGRisks2227-90912021-06-019611210.3390/risks9060112Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims ReservingNataliya Chukhrova0Arne Johannssen1Faculty of Business Administration, University of Hamburg, 20146 Hamburg, GermanyFaculty of Business Administration, University of Hamburg, 20146 Hamburg, GermanyIn 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.https://www.mdpi.com/2227-9091/9/6/112adaptive learningdependence modelingevolutionary modelsinsuranceKalman filtermachine learning |
| spellingShingle | Nataliya Chukhrova Arne Johannssen Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving Risks adaptive learning dependence modeling evolutionary models insurance Kalman filter machine learning |
| title | Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving |
| title_full | Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving |
| title_fullStr | Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving |
| title_full_unstemmed | Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving |
| title_short | Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving |
| title_sort | kalman filter learning algorithms and state space representations for stochastic claims reserving |
| topic | adaptive learning dependence modeling evolutionary models insurance Kalman filter machine learning |
| url | https://www.mdpi.com/2227-9091/9/6/112 |
| work_keys_str_mv | AT nataliyachukhrova kalmanfilterlearningalgorithmsandstatespacerepresentationsforstochasticclaimsreserving AT arnejohannssen kalmanfilterlearningalgorithmsandstatespacerepresentationsforstochasticclaimsreserving |