Application of Quantum-behaved Particle Swarm Optimization in Parameter Estimation of Option Pricing

摘要：

Due to the nonlinear of the Black-Scholes option pricing model, r and σ were not easy to be solved by analytic method. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm was proposed to estimate the parameters because of its global search ability and robustness. In the process of optimization, Black-Scholes option pricing formula was used as the research object to establish the algorithm model of parameter estimation and weighted sum of squared errors between experimental values and predicted values was used as the objective optimization function. Experimental results show that QPSO algorithm is more effectively than Particle Swarm Optimization (PSO) algorithm and Deferential Evolution (DE) algorithm.

关键词： QPSO Option Pricing Parameter estimation Black-Scholes partial differential equation

作者: Xia Zhao Jun Sun Wenbo Xu

作者单位: Department of Information Technology Jiangnan University Wuxi, China

会议类型: 国际会议

会议名称: 第九届分布式计算及其应用国际学术研讨会

会议地点: 香港

会议语种:英文

页码: 10-12

在线出版日期: 2010-08-12（万方平台首次上网日期，不代表论文的发表时间）