摘要
鉴于证券资产价格变化序列中存在非线性、确定性及混沌现象,本文将信号学中基于相空间重构理论下噪音水平的估计思想引入证券市场并构建了相应的估计模型,然后在检验2010年1月4日到2010年12月14日期间沪深300指数为例的高频资产价格变化序列数据的非线性、确定性及混沌特征的基础上估计了其日噪音水平,结果表明沪深300指数期间噪音处于21.55%-65.40%之间,且噪音水平存在右偏及扁平特征,与资产价格走势及市场信息之间可能存在复杂的关系。
Given the nonlinear,deterministic and chaotic phenomena existing in the price changing series of securities assets,the article introduces the thought of noise level estimating method,which is based on phase space reconstruction theory from signal science,into securities market and builds a corresponding estimating model.After testing the nonlinear,deterministic and chaotic characteristics of HS300 index from January 4th 2010 to December 14th 2010 by using high frequency data,the article estimates the daily noise level.The results show that the noise level of HS300 index is 21.55%-65.40%,right skew,and has complicated relationship with the trend of asset price and market information.
出处
《上海金融》
CSSCI
北大核心
2013年第1期91-94,55,共5页
Shanghai Finance
基金
国家自然科学基金项目(71271146)
长江学者和创新团队发展计划项目资助(IRT1028)
关键词
噪音水平
相空间重构
最小邻距
证券市场
Noise Level
Phase Reconstruction
Minimum Neighboring Distance
Securities Market
