Catastrophe insurance portfolio optimization and information management

Quantifying losses inferred from natural catastrophes is a crucial part in our ability to understand and manage the damage caused by these catastrophic events. A significant component in reducing the uncertainty present in loss estimation can be associated with the use of better information in relation with such factors as exact building geometry, construction quality, design, and vulnerability analysis. The question remains though, how to judge whether the enhancement in the losses accuracy justifies the cost of obtaining this improved information.; This study is an effort towards presenting a procedure for integrating, analyzing and evaluating the impact of improved losses information on insurance portfolio-related decisions. A conceptual methodology is proposed in the aim to help insurers decide on the optimal information resolution that is best-suited for the portfolio analysis. The sensitivity analysis emphasizes on the error between simulated losses obtained from default building data versus losses obtained from enhanced information and how this error translates into misleading insurance objectives predictions. For that matter, an insurance portfolio optimization problem is also suggested offering to maximize profit and control exposure risk. Here two new components are incorporated, the means to control the correlation among losses and the ability to reach a geographically and structurally resolved portfolio.; To solve the optimization problem, a multi-scale model is presented. A coarse model and a refined model are established to uncover the optimal design variables. The models work in chain and are intended to save on timely computations and are supposed to fully recapture the characteristics of the initial model. Losses aggregation schemes are also examined to help reduce the dimension of the optimization problem and speed up the solution process.