A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC) algorithm is a popular numerical method to approach solutions of such stochastic control problems as analytical solutions are not tractable...

This paper proposes a novel and practical approach of addressing optimal reinsurance via an empirical approach. This method formulates reinsurance models using the observed data directly and has advantages including (i) transformation of an infinite dimensional optimization problem to finite...

In this paper, we propose to combine the Marginal Indemnification Function (MIF) formulation and the Lagrangian dual method to solve optimal reinsurance model with distortion risk measure and distortion reinsurance premium principle. The MIF method exploits the absolute continuity of admissible...

An innovative cumulative distribution function (CDF) based method is proposed for deriving optimal reinsurance contracts to maximize an insurer's survival probability. The optimal reinsurance model is a non-concave constrained stochastic maximization problem, and the CDF based method transforms...