Double chain ladder. By adding the information of reported count data to a classical triangle of reserving data, we derive a suprisingly simple method for forecasting IBNR and RBNS claims. A simple relationship between development factors allows to involve and then estimate the reporting and payment delay. Bootstrap methods provide prediction errors and make possible the inference about IBNR and RBNS claims, separately.

References in zbMATH (referenced in 27 articles , 1 standard article )

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  1. Avanzi, Benjamin; Taylor, Greg; Wang, Melantha; Wong, Bernard: \textttSynthETIC: an individual insurance claim simulator with feature control (2021)
  2. Gabrielli, Andrea: An individual claims reserving model for reported claims (2021)
  3. Gao, Guangyuan; Meng, Shengwang; Shi, Yanlin: Dispersion modelling of outstanding claims with double Poisson regression models (2021)
  4. Gabrielli, Andrea: A neural network boosted double overdispersed Poisson claims reserving model (2020)
  5. Lindholm, Mathias; Verrall, Richard: Regression based reserving models and partial information (2020)
  6. Wahl, Felix: Explicit moments for a class of micro-models in non-life insurance (2019)
  7. Wahl, Felix; Lindholm, Mathias; Verrall, Richard: The collective reserving model (2019)
  8. Woundjiagué, Apollinaire; Mbele Bidima, Martin Le Doux; Mwangi, Ronald Waweru: An estimation of a hybrid log-Poisson regression using a quadratic optimization program for optimal loss reserving in insurance (2019)
  9. Gigante, Patrizia; Picech, Liviana; Sigalotti, Luciano: A mixture model for payments and payment numbers in claims reserving (2018)
  10. Lee, Y. K.; Mammen, E.; Nielsen, J. P.; Park, B. U.: In-sample forecasting: a brief review and new algorithms (2018)
  11. Margraf, Carolin; Elpidorou, Valandis; Verrall, Richard: Claims reserving in the presence of excess-of-loss reinsurance using micro models based on aggregate data (2018)
  12. Meng, Shengwang; Gao, Guangyuan: Compound Poisson claims reserving models: extensions and inference (2018)
  13. Portugal, Luís; Pantelous, Athanasios A.; Assa, Hirbod: Claims reserving with a stochastic vector projection (2018)
  14. Wüthrich, Mario V.: Machine learning in individual claims reserving (2018)
  15. Hiabu, M.: On the relationship between classical chain ladder and granular reserving (2017)
  16. Bissantz, Nicolai; Dette, Holger; Hildebrandt, Thimo; Bissantz, Kathrin: Smooth backfitting in additive inverse regression (2016)
  17. Huang, Jinlong; Wu, Xianyi; Zhou, Xian: Asymptotic behaviors of stochastic reserving: aggregate versus individual models (2016)
  18. Godecharle, Els; Antonio, Katrien: Reserving by conditioning on markers of individual claims: a case study using historical simulation (2015)
  19. Kuang, D.; Nielsen, B.; Nielsen, J. P.: The geometric chain-ladder (2015)
  20. Lee, Young K.; Mammen, Enno; Nielsen, Jens P.; Park, Byeong U.: Asymptotics for in-sample density forecasting (2015)

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