中国股票市场的交易与信息——基于自回归条件持续时间标值模型的实证研究

发布时间：2018-05-29 09:04

本文选题：市场微观结构 + 交易持续时间　；参考：《财经科学》2010年07期

【摘要】：构建多元线性和半参数单指数自回归条件持续时间标值模型及其估计方法,基于分笔交易数据研究中国股票市场交易与信息之间的线性与非线性动态关系。实证结果表明:(1)交易持续时间存在明显正自相关性、过度分散性和聚集效应;(2)滞后收益率、成交量、买卖价差对交易持续时间有显著线性正影响,滞后波动率对交易持续时间有显著线性负影响,各滞后市场微观结构特征变量对交易持续时间的影响普遍支持Easley和O’Hara(1992)"无交易预示着无消息"的发现;(3)滞后收益率、波动率、成交量和买卖价差对交易持续时间的非线性正、负影响有差异,各滞后市场微观结构特征变量对交易持续时间的影响没有一致性的结论,Diamond和Verrechia(1987)的"无交易预示着坏消息"以及Easley和O’Hara(1992)的"无交易预示着无消息"的结论同时成立。
[Abstract]:The multivariate linear and semi-parametric exponential autoregressive conditional duration scale model and its estimation method are constructed. The linear and nonlinear dynamic relationship between trading and information in Chinese stock market is studied based on the data of split trades. The empirical results show that the trading duration has a significant positive autocorrelation, overdispersion and aggregation effect are significant linear positive effects on the trading duration, such as the lagging yield, the volume of the transaction, and the spread of the buying and selling price. Lag volatility has a significant linear negative effect on the trading duration. The influence of each lag market microstructure characteristic variable on the trading duration generally supports the discovery of "no trading indicates no news" (Easley and Ohn Hara 1992) the lagging rate of return and volatility. The nonlinear positive and negative effects of trading volume and spread on the duration of trading are different. Conclusion that there is no consistency between the effects of the structural characteristics of the lagging markets on the duration of trading. Diamond and Verrechia1987) the conclusion that "no transaction indicates bad news" and "no transaction indicates no news" by Easley and Ohn Hara1992) holds true at the same time.
【作者单位】：西南财经大学统计学院;
【基金】：2009年教育部人文社会科学研究青年基金项目《新兴订单驱动市场金融持续时间的统计分析及其应用》(项目批准号:09YJC910009) 西南财经大学“211工程”三期青年教师成长项目(211QN09020),西南财经大学“211工程三期”统计学重点学科建设项目的资助
【分类号】：F224;F832.51

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