噪声方差、跳跃波动、连续波动的分离及其相互关系——来自中国股市的实证研究  

Separating noise variance from jump volatility and continuous volatility and the relationships among them:Evidence from the Chinese stock market

作　　者：张传海 ZHANG Chuanhai(School of Finance,Zhongnan University of Economics and Law,Wuhan 430073,China)

机构地区：[1]中南财经政法大学金融学院,湖北武汉430073

出　　处：《管理工程学报》2021年第4期117-131,共15页Journal of Industrial Engineering and Engineering Management

基　　金：教育部人文社会科学研究青年基金项目(18YJC790210);中央高校基本科研业务经费(2722019PY038)。

摘　　要：本文在市场微观结构噪声和跳跃下将Jing等[1]中的预平均门限已实现方差这一连续波动估计拓展到允许噪声过程的条件方差为时变的情形。我们分别采用预平均和门限的方法消除噪声和跳跃的影响,同时给出其渐近性质。蒙特卡罗模拟结果表明这一估计对噪声和Lévy跳跃稳健并且相比同类连续波动估计具有更好的表现。鉴于这一估计的优良性质,本文还通过蒙特卡罗模拟探讨门限和预平均水平的选取以及移动平均噪声,舍入噪声和相依结构噪声等时变条件方差噪声对该估计的影响。在实证分析中,本文首先基于这一改进的估计从2014到2015年上证50成分股Level-2的逐笔交易数据中分离出噪声方差、跳跃波动和连续波动。然后,采用面板向量自回归模型研究三者之间相互关系,发现噪声方差与连续波动之间存在显著正的双向Granger因果关系;连续波动和噪声方差均是跳跃波动的Granger原因,但是反过来不成立。最后,利用拓展的异质自回归(HAR)模型来研究噪声方差对连续波动和跳跃波动的预测能力,发现噪声方差对有效价格的波动具有正向预测作用,该预测作用不仅针对连续波动,而且对跳跃波动具有显著预测能力,但前者显著性更强。Volatility modeling is one of the core topics in finance and financial econometrics.It has wide applications in risk management,asset pricing,and asset allocation.In recent years,extreme volatilities occurred in the Chinese financial market from time to time.For example,during 2014-2015,the Chinese stock market underwent extreme market conditions:the stock market sharp rally was followed by a sharp plunged,which eventually evolved into a crash.In particular,the abnormal volatilities from June to August in 2015 caused investors and regulators to concern about financial risks.Existing volatility modeling methodologies are largely based on low-frequency data.The most widely used models include GARCH and stochastic volatility(SV)models.Although these models can capture the main characteristics of financial market volatility,they are subject to a few limitations.First,these models are parametric in spirit and thus may suffer from model misspecification.Second,these models only use low frequency returns to estimate and forecast volatility without considering intraday sample information.The estimates may be biased,leading to nontrivial forecast errors.Last,the parametric estimation procedure for these models remains very complicated,especially for the SV model.In the multivariate case,these models are constantly challenged by the curse of dimensionality,and therefore are of limited use in real applications.Recently,with easier access to high-frequency data and the rise of high frequency trading,an increasing number of studies begin to use high-frequency data to estimate,model and predict volatility.One of the most famous proxy variables for volatility is so-called realized volatility(RV).RV has several merits:it is nonparametric and thus requires less assumptions on model set-up.As a matter of fact,it only imposes the assumption of no arbitrage opportunities.Besides,RV is a direct proxy for volatility,it is not necessary to obtain the embedded volatility like the GARCH model.Last but not least,many studies find the fore

关 键 词：噪声方差 跳跃波动 连续波动 面板向量自回归模型 市场微观结构 

分 类 号：F830.9[经济管理—金融学]