中国股指现货和期货市场的日内波动与交易量:基于高频数据的证据  

Intraday Volatility and Volume in Chinese Stock Index Cash and Futures Markets: Evidence from High Frequency Data

作　　者：西村友作[1] 孙便霞 

机构地区：[1]对外经济贸易大学国际经济研究院,北京100029 [2]南方科技大学金融数学与金融工程系,广东深圳518055

出　　处：《管理工程学报》2016年第2期93-100,共8页Journal of Industrial Engineering and Engineering Management

基　　金：国家博士后科学基金资助面上项目(2013M530167)

摘　　要：随着金融高频交易的快速发展,日内金融资产价格的形成机制与市场微观结构发生了巨大变化。本文着眼于中国股票现货市场与期货市场的波动率和交易量的日内动态关系,基于沪深300股指现货与期货的5分钟数据,利用FFF回归与AR-FIEGARCH-V模型对日内波动率与交易量之间的动态关系进行了实证分析。本文的主要结论为:中国股票现货与期货市场的日内波动率,除了受到其本身的日内交易量的正向影响之外,还受到跨市场交易量的正向影响;同时,期货市场对现货市场的信息传导强度远大于现货市场对期货市场的信息传导强度,说明市场间的信息传导强度是不对称的。Along with the rapid development of high frequency trading in financial markets, asset price forming mechanism and market microstructures have changed greatly. Thus, the study on information transmitting mechanism between financial markets becomes more important. Price movements and volume changes reflect information flow in financial markets. Targeting the CSI 300 Index and Index Futures markets, this paper studies information transmitting mechanisms between Chinese stock index cash and futures markets. The data sample ranges from Apr 16, 2010 to Feb 13, 2012, covering 442 trading days in total.Much present research has displayed periodic characteristics among intraday financial volatility. This periodicity must be taken into account when modeling intraday volatility. The descriptive analysis based on 5-min interval data reveals a U shape for intraday volatility of the stock index cash market, and a 3V（or VVV） shape for that of the corresponding futures market. According to Andersen and Bollerslev（1997）, we use the FFF regression method to preprocess high frequency return series to remove the intraday volatility periodicity. This paper attempts to answer two questions. First question is whether the intraday volatility of CSI 300 Index cash（futures） markets is influenced by internal intraday trading volume. Second question is whether intraday volatility in one market is influenced by external trading volume in the other market.Considering long memory characteristic of intraday volatility series, this paper adopts the FIEGARCH model（Bollerslev and Mikkelsen, 1996） and introduces intraday volume change into the variance equation of the research model. Consequently, the AR-FIEGARCH-V model is built to study the influence of intraday trading volume on intraday volatility in（between） Chinese stock index cash and futures markets. Empirical study shows that intraday trading volume positively influences the volatility of the same market, which is in accord with the results of much present work using d

关 键 词：股指期货 高频数据 日内周期性 FFF回归 

分 类 号：F830[经济管理—金融学]