Empirical Study on Stock Market Through ARCH Model Based on Particle Swarm Optimization
In this paper, we discuss the properties of ARCH model and propose an approach for estimating the parameters in ARCH model, the method of maximum likelihood (ML) with particle swarm optimization(PSO). The purposes of using PSO algorithm is to obtain the global maximum value of likelihood function as well as to make ARCH model applicable to immediate disposition of a vast amount of data from financial market. The ARCH model based on PSO is applied to empirical study of real assets returns analysis. The sample data we use in this study is Dow Jones Industrial Average index return. Results show that the PSO-based ARCH model is effective in predicting the volatility in the US Stock Market.
Time Series ARCH Model PSO Stock Market
Jun Sun C.-H.Lai Wenbo Xu
School of Information Technology, Southern Yangtze University, No.1800, Lihu Dadao, Wuxi Jiangsu 214 School of Computing and Mathematical Sciences, University of Greenwich, Greenwich, London SE109LS, U
国际会议
杭州
英文
827-831
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）