Name
Abstract | ||
|---|---|---|
Risk hedging strategies are at the heart of financial risk management. As with many financial institutions, insurance companies try to hedge their risk against potentially large losses, such as those associated with natural catastrophes. Much of this hedging is facilitated by engaging in risk transfer contracts with the global reinsurance market. Devising an effective hedging strategy depends on careful data analysis and optimization. In this paper, we study from the perspective of an insurance company the Dynamic Reinsurance Optimization problem in which given a set of expected loss distributions (the result of running a Catastrophic Loss Model), a model of reinsurance market costs, and some general financial terms, our task is to evolve a set of complex multi-layered reinsurance contracts that define a Pareto frontier quantifying the best available tradeoffs between expected risk and returns for the insurer. Our approach to this reinsurance contract optimization problem is three fold. Firstly, we apply the Strength Pareto Evolutionary Algorithm 2 (SPEA2) meta-heuristic to guide the multi-objective search process. Secondly, we exploit equation reordering to minimize computation, aggressively pre-computation/caching methods, and discretization to efficiently evaluate individual solutions. Lastly, we apply High Performance Computing (HPC) techniques including shared memory parallelization, vectorization and data prefetching to accelerate the search process. As a result, our prototype Dynamic Reinsurance Optimizer is able to solve industrial sized problems on a single multi-core server in about 2 minutes for 7 layers and 4 minutes for 15 layers per run.
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Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2695664.2695899 | SAC 2015: Symposium on Applied Computing
Salamanca
Spain
April, 2015 |
Keywords | Field | DocType |
Risk Management, Dynamic Reinsurance Contract Optimization, Treaty, SPEA2, High Performance Computing | Financial risk management,Expected loss,Mathematical optimization,Reinsurance,Evolutionary algorithm,Computer science,Risk management,Hedge (finance),Optimization problem,Pareto principle | Conference |
ISBN | Citations | PageRank |
978-1-4503-3196-8 | 1 | 0.41 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoxu Wang | 1 | 1 | 0.41 |
| Omar Andrés Carmona Cortes | 2 | 8 | 2.69 |
| Andrew Rau-chaplin | 3 | 638 | 61.65 |