基于ACD模型的中国期货市场波动性被引量：11

Volatility of Chinese futures market based on ACD model

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摘要通过用久期来调整收益率,把非等距数据等距化,构建ACD-GARCH模型来反映高频波动特征.并添加微观结构变量,构建了ACD-GARCH-M模型,用以分析久期、交易量与收益率和波动率的关系.结果表明:较长的久期是由于信息缺乏所致,久期对收益率的影响不显著,但久期和价格的波动性负相关.交易量和价格的波动性正相关.在加入了微观解释变量的ACD-GARCH-M模型中,GARCH效应大大减弱了,说明ACD-GARCH-M模型能较好地反映高频波动聚集性的本质,久期、交易量是产生波动聚集的原因. Before the building of ACD-GRACH model to research the high frequency volatility, it adjusted the yield by duration to equal the distance of data. It added micro-structural variables to build the ACD-GARCH-M models and analyze the relationship among duration, volume, yield and volatility. The results reveal that long duration results from the lack of information, and there are insignificant impacts on yield from duration; negative relationship between duration and price volatility; and positive relationship between volume and price volatility. ACD-GARCH-M model which contains explanatory variables demonstrate less GARCH effects implies that it could describe the volatility clustering of high frequency data better, and duration and volume can explain the volatility clustering to a large extent.

作者刘向丽成思危汪寿阳洪永淼

机构地区中央财经大学金融学院中国科学院研究生院管理学院中国科学院数学与系统科学研究院美国康乃尔大学经济学系与统计科学系

出处《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2012年第2期268-273,共6页 Systems Engineering-Theory & Practice

基金国家自然科学基金(71071170) 教育部新世纪优秀人才支持计划中财121人才工程青年博士发展基金(QB-JJJ201003) 中央财经大学青年科研创新团队中央财经大学211工程三期科研基金项目

关键词高频数据久期日内效应 ACD-GARCH模型 high frequency data duration intraday effect ACD-GARCH model

分类号 F830 [经济管理—金融学]

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