The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

中文翻译：

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基于混合启发式算法的金融市场网络复杂网络层次结构计算

最近的经验研究表明，银行间市场中的货币中心网络更加稳健。因此，分层金融网络的研究是系统风险管理的关键部分。在先前的研究中已经提出了各种方法来找到银行间网络到核心和外围子集的最佳划分。但是，这些已被近似方法采用的方法通常不能保证最优分割。提出了一种遗传模拟退火算法作为混合启发式算法来检测银行同业网络中的层次结构，同时分析了其效果。首先以数学方式开发了核心外围模型的错误评分的优化，作为优化函数的改进表达，该算法将遗传算法结合到模拟退火算法中，以确保最佳划分并从局部优化中跳出来。最后通过银行间网络的经验分析验证了该算法的结果。通过风险扩散模型下的免疫策略，验证了核心外围结构对风险管理的意义。