Forecasting reserve risk for temporal dependent losses in insurance

Sawssen Araichi, Christian de Peretti (christian.de-peretti@univ-lyon1.fr) and Lotfi Belkacem
Additional contact information
Sawssen Araichi: LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon
Christian de Peretti: ECL - École Centrale de Lyon - Université de Lyon, LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon
Lotfi Belkacem: Université de Sousse

Post-Print from HAL

Abstract: Abstract In non‐life insurance, insurance companies aim to accurately assess their reserves in order to fulfil their future obligations. They are based on methods provided by the literature review to evaluate their reserve risk. However, these methods do not take all claim characteristics and ignore the temporal dependence structure of claims, which can affect reserve amounts and lead to delayed payments for policyholders. Therefore, the aim is to investigate the temporal dependence structure among claim amounts (losses) in order to evaluate the accurate amounts of reserves. To achieve this goal, a model called the Generalized Autoregressive Conditional Sinistrality Model is proposed, which considers the temporal dependence characteristics of claims. This model is used to estimate model parameters, so the consistency of such an estimate is proven. Additionally, a bootstrap method adjusted to the Generalized Autoregressive Conditional Sinistrality model is proposed for predicting reserves and errors. The results reveal that considering temporal dependence between losses improves reserve distribution estimation and enhances solvency capital requirement. This means that insurance companies will be able to ensure they have sufficient funds available to meet their obligations to policyholders, thereby enhancing customer satisfaction and trust. Additionally, this can assist insurance companies in maintaining better regulatory compliance.

Date: 2024-06-26
References: Add references at CitEc
Citations:

Published in International Journal of Finance and Economics, 2024, ⟨10.1002/ijfe.3014⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04875444

DOI: 10.1002/ijfe.3014

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD (hal@ccsd.cnrs.fr).