作者：***
来源：《金融理论探索》2016年第03期
摘 要：以2001年1月1日至2015年7月31日上证综指日收盘价为样本，运用近似熵-小波变换方法，对上证综指时间序列进行了多尺度复杂性分析。通过计算样本数据序列的近似熵值发现，股票市场的波动性与股指时间序列的近似熵值密切相关：近似熵值越大，股市波动性越大。通过引入小波变换对近似熵序列进行分解和重构，发现股价波动具有阶段聚集性，而且股价变化过程中的异常波动伴随着重大经济事件或突发事件而发生。 一水硫酸锌 关 键 词：近似熵；小波变换；股价波动；波动集聚性
中图分类号：F224 文献标识码：A 文章编号：1006-3544（2016）03-0039-07
Abnormal Volatility and Volatility Clustering of Stock Price
——an Empirical Study Based on Approximate Entropy and Wavelet Transform
钟点丈夫
阿基米德定律 Guo Jianhua
（Shaoyang University， Shaoyang 422000， China）
x1650gt
Abstract： The paper selected daily closing price of shanghai composite index from January 1st 2001 to July 31st 2015 as samples， adopted the method of approximate entropy of wavelet transform and carried out multi-scale analysis of the time series of Shanghai Composite Index’ complexity. After calculating data series’ approximate entropy， it could be concluded that there is a close connection between stock market volatility and approximate entropy of stock index’s time series. The greater the approximate entropy is， the more volatile of the stock market will be. Through the introduction of wavelet transform， the paper tries to disintegrate and rebuild approximate entropy series， and discovers that there are phased clustering with the stock price volatility. And the occurrence of abnormal volatility of stock price is always accompanied with major economic incidents or emergent incidents.
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