Stochastic Claims Reserving Methods with State Space Representations: A Review

Stochastic Claims Reserving in General Insurance

This paper considers a wide range of stochastic reserving models for use in general insurance, beginning with stochastic models which reproduce the traditional chain-ladder reserve estimates. The models are extended to consider parametric curves and smoothing models for the shape of the development run-off, which allow extrapolation for the estimation of tail factors. The Bornhuetter-Ferguson t...

A Simulation Procedure for Comparing Different Claims Reserving Methods

Recent Development in Claims Reserving

This contribution deals with recent development in the field of mathematical loss reserving via Chain Ladder that is regarded as the most popular method for setting technical reserves in non life insurance. It could be formulated deterministically or via a stochastic model. However there are some drawbacks of using this method automatically that will be discussed. Its generalisation Munich Chai...

Stochastic Model Predictive Control: State space methods

1 Performance objective and closed-loop convergence 1 1.1 Stochastic system models . . . . . . . . . . . . . . . . . . . 1 1.2 Performance cost . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Cost evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Unconstrained optimal control . . . . . . . . . . . . . . . . . 12 1.5 Receding horizon control, stability and convergence . . ...

Predictive State Representations with State Space Partitioning

Predictive state representations (PSRs) are powerful methods of modeling dynamical systems by representing state through observational data. Most of the current PSR techniques focus on learning a complete PSR model from the entire state space. Consequently, the techniques are often not scalable due to the dimensional curse, which limits applications of PSR. In this paper, we propose a new PSR l...