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计算机工程 ›› 2007, Vol. 33 ›› Issue (24): 29-31. doi: 10.3969/j.issn.1000-3428.2007.24.010

• 博士论文 • 上一篇    下一篇

基于QPSO的上证指数ARCH模型

梅 娟,孙 俊,须文波   

  1. 江南大学信息工程学院,无锡 214122
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-12-20 发布日期:2007-12-20

ARCH Model for Stock Return of Shanghai
Based on QPSO Algorithm

MEI Juan, SUN Jun, XU Wen-bo   

  1. School of Information Technology, Southern Yangtze University, Wuxi 214122
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-12-20 Published:2007-12-20

摘要: 介绍一种利用量子行为粒子群算法(QPSO)建立上证指数收益的 ARCH模型,利用不同的算法精确地估计模型中的参数,验证QPSO算法的优越性。利用得到的估计模型对指数收益进行预测,得到大致跟随指数实际走势的预测值。试验结果表明,QPSO算法比粒子群算法、遗传算法能更好地解决此类问题。

关键词: ARCH模型, QPSO算法, PSO算法, 异方差, 遗传算法

Abstract: This paper proposes an improved quantum-behaved particle swarm optimization using the notion of species for establishing the ARCH model for stock return, and then forecastes subsequent trend. The experimental results show quantum-behaved particle swarm optimization is better at solving this problem than PSO and GA.

Key words: Auto-Regressive Conditional Heteroskedasticity(ARCH)model, Quantum-Behaved Particle Swarm Optimization(QPSO)algorithm, Particle Swarm Optimization(PSO)algorithm, heteroskedasticity, genetic algorithm

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