Name
Abstract | ||
|---|---|---|
In this paper, we study from the perspective of an insurance company the Reinsurance Contract Placement problem. Given a reinsurance contract consisting of a fixed number of layers and a set of expected loss distributions (one per layer) as produced by a Catastrophe Model, plus a model of current costs in the global reinsurance market, identifying optimal combinations of placements (percent shares of subcontracts) such that for a given expected return the associated risk value is minimized. Our approach explores the use bio-inspired metaheuristics with the goal of determining which evolutionary optimization approach leads to the best results for this problem, while being executable in a reasonable amount of time on realistic industrial sized problems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45523-4_19 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Reinsurance Analytics,Reinsurance Contract Placement,Particle Swarm Optimization,Differential Evolution,Population Based,Incremental Learning,Financial Risk,Optimization | Particle swarm optimization,Catastrophe modeling,Expected loss,Reinsurance,Mathematical optimization,Computer science,Differential evolution,Population-based incremental learning,Expected return,Metaheuristic | Conference |
Volume | ISSN | Citations |
8602 | 0302-9743 | 2 |
PageRank | References | Authors |
0.43 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar Andrés Carmona Cortes | 1 | 8 | 2.69 |
| Andrew Rau-chaplin | 2 | 638 | 61.65 |
| Duane Wilson | 3 | 3 | 0.82 |
| Jürgen Gaiser-Porter | 4 | 3 | 0.82 |