| 基于ACD模型的中国期货市场波动性 |
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| 引用本文： | 刘向丽,成思危,汪寿阳,洪永淼.基于ACD模型的中国期货市场波动性[J].系统工程理论与实践,2012,32(2):268-273. |
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| 作者姓名： | 刘向丽 成思危 汪寿阳 洪永淼 |
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| 作者单位： | 1. 中央财经大学 金融学院, 北京 100081;
2. 中国科学院研究生院 管理学院, 北京 100190;
3. 中国科学院数学与系统科学研究院, 北京 100190;
4. 美国康乃尔大学 经济学系与统计科学系, 纽约 14850 |
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| 基金项目： | 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 |
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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.
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| 关 键 词： | 高频数据 久期 日内效应 ACD-GARCH模型 |
| 收稿时间： | 2009-06-09 |
Volatility of Chinese futures market based on ACD model |
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| LIU Xiang-li
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CHENG Si-wei
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WANG Shou-yang
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HONG Yong-miao.Volatility of Chinese futures market based on ACD model[J].Systems Engineering —Theory & Practice,2012,32(2):268-273. |
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| Authors: | LIU Xiang-li CHENG Si-wei WANG Shou-yang HONG Yong-miao |
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| Institution: | 1. School of Finance, Central University of Finance and Economics, Beijing 100081, China;
2. School of Management, Graduate University of Chinese Academy of Sciences, Beijing 100190, China;
3. Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China;
4. Department of Economics, Cornell University, New York 14850, USA |
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| Abstract: | 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. |
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| Keywords: | high frequency data duration intraday effect ACD-GARCH model |
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