结构VAR模型辨识的条件互信息图模型

在结构向量自回归(VAR)模型辨识的图模型中引入信息论方法.定义了线性条件互信息图,图中的结点表示时间序列不同时刻的随机变量,结点间的边表示随机变量之间存在的因果相依关系.提出了随机变量之间条件线性联系存在性的信息论检验方法.图中边的存在性用基于线性条件互信息的枢轴量检验,枢轴量的显著性用置换检验决定.用统计分析的方法确定当前变量之间联系的方向,建立了有向非循环图.最后以模拟序列为例,验证了所提出的方法是可行且有效的.     

基于非线性Granger因果检验的中国大陆和世界其他主要股票市场之间的信息溢出

采用线性及非线性Granger因果检验的方法,对中国大陆股票市场和世界其他主要股票市场之间不同阶段的信息溢出现象进行了实证研究.通过比较Granger (1969)线性因果检验和 Hiemstra and Jones(1994)非线性因果检验, 发现中国大陆市场和世界其他主要股票市场之间存在非线性信息溢出效应;随着中国大陆市场改革的深入和全球经济一体化的发展,信息溢出的程度也在提升; 在建立风险预警机制时,需要充分考虑其他市场的信息.     

Did speculative activities contribute to high crude oil prices during 1993 to 2008?

By applying two nonlinear Granger causality testing methods and rolling window strategy to explore the relationship between speculative activities and crude oil prices, the unidirectional Granger causality from speculative activities to returns of crude oil prices during the high price phase is discovered. It is proved that speculative activities did contribute to high crude oil prices after the Asian financial crisis and OPEC's output cut in 1998. The unidirectional Granger causality from returns of crude oil prices to speculative activities is significant in general. But after 2000, with the sharp rise in crude oil prices, this unidirectional Granger causality became a complex nonlinear relationship, which cannot be detected by any linear Granger causality test.     

非线性协整关系及其检验方法研究

文献「４」所提出的协整概念描述了向量时间序列中的长期线性均衡关系,从而可以称为“线性协整”。然而,经济系统中的许多时间序列是长记忆的,这些序列本身以及序一是往往存在非线性关系,所以对这些序列进行线性协整分析是不太合适的。     

Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data, stock markets are closely connected with Internet big data from diverse sources. This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines, public media and social media. To achieve this purpose, a big data-based causality testing framework is proposed with three steps, i.e., data crawling,data mining and causality testing. Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets, web search data, news, and microblogs as samples of Internet big data, some interesting findings can be obtained. 1) There is a strong bi-directional, linear and nonlinear Granger causality between stock markets and investors' web search behaviors due to some similar trends and uncertain factors. 2) News sentiments from public media have Granger causality with stock markets in a bi-directional linear way, while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way, running from stock markets to microblog sentiments.3) News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority. The results of this paper might provide some valuable information for both stock market investors and modelers.     

Learning multivariate time series causal graphs based on conditional mutual information

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.     

基于HAC估计视角的格兰杰伪因果关系检验

研究发现相互独立的弱平稳过程之间会产生伪因果关系, 经传统HAC法修正的Wald统计量甚至存在更高的伪因果关系概率. 文章认为数据过程的长期方差是发生伪因果关系的深层次原因, 通过改进传统HAC法的截断参数, 能获得格兰杰因果关系检验统计量(Wald)的不依赖于冗余参数的极限分布. 针对设定各种弱平稳过程并利用模拟技术, 研究发现新的Wald^*统计量大大减少了发生伪因果关系的概率, 并且对于数据过程持久性和样本容量是稳健的, 但是存在一定的检验水平扭曲.     

中国科技投入与经济增长的Granger因果关系分析

传统的Granger因果关系检验只适用于平稳变量。中国经济的快速增长使得多数宏观经济变量表现出非平稳特征。协整以及建立在协整关系基础上的误差修正模型为研究非平稳变量之间的因果关系指明了新的途径。本文利用1953～2003年中国科技投入和经济增长的年度数据，建立了一个反映二者动态关系的误差修正模型。通过基于误差修正模型的Granger因果检验，发现中国科技投入和经济增长之间存在双向因果关系。     

Testing linear and nonlinear granger causality in CSI300 futures and spot markets based on new concepts of nonlinear positive/negative spillover

supported by the National Natural Science Foundation of China under Grant Nos.71125005 70871108 and 70810107020;; Outstanding Talents Funds of Organization Department Beijing Committee of CPC     

A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.     

我国棉花期货与现货市场的价格发现与波动溢出效应

以研究我国棉花期货和现货市场的动态关系为目的, 基于 VEC 模型、Granger 因果检验、脉冲响应分析和 BEKK 模型, 对我国棉花期货和现货市场的价格发现功能和波动溢出效应进行实证分析. 研究结果表明: 期货价格和现货价格之间存在长期均衡关系和双向Granger 引导关系. 但期货市场对现货市场的引导作用更强, 并且较现货市场具有更强的信息效应. 此外, 两个市场均存在很强的自身波动滞后效应, 相互间的波动溢出效应也非常显著, 但期货市场对现货市场的波动溢出效应明显大于后者对前者的波动溢出效应.     

Information Spillovers among International Crude Oil Markets — An Empirical Analysis Based on CCF Method and ECM

Using Hong's method based on Cross Covariance Function (CCF) and Error Correction Model (ECM), this article studies Granger causality and information spillovers among major global crude oil markets including London, New York, Dubai as well as Tapis and Minas in Southeast Asia. The results show that London and New York futures markets play dominant roles in information spillover, and WTI crude oil futures has a slight edge over Brent crude oil futures in information transmission by using methodology introduced by Hong. In addition, empirical results show Hong's method is more effective than ECM in testing Granger causality and information spillovers.     

基于非参数回归的金融传染检验

本文借助特征函数的优良性质,基于非参数回归构造了金融传染的检验统计量.与现有文献相比,该统计量不仅避免了模型设定偏误问题,而且能够同时捕获线性和各种形式的非线性传染效应.在原假设成立时,该统计量渐近服从于标准正态分布.数值模拟结果表明,该统计量具有良好的有限样本性质,能够识别多种形式的非线性金融传染.本文进一步应用该统计量探讨了中国金融市场与东亚、拉丁美洲、新兴市场国家之间的传染效应,捕获了传统基于线性测度方法无法刻画的非线性传染效应,说明我国与这些金融市场之间存在显著的非线性传染效应.     

中国证券市场波动与收益的非线性相关

考察我国沪深两市指数收益的序列独立性.表明证券市场收益序列尽管线性自相关现象不显著,却存在着明显的非线性相关关系.而且这种非线性相关关系一般可由“ARCH效应”来加以解释,即波动聚类性是形成非线性相关的主要原因.应用BDS统计方法对收益残差的检验显示,GARCH(1,1)形式能够很好地刻画上证综合指数的非线性和波动性特征,ARCH(3)形式则能够很好地刻画深证综合指数的生成过程.     

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.     

基于核函数的混沌序列局域线性预测方法

针对局域线性预测方法本质上是用较简单的非线性函数来预测高度非线性的混沌时间序列的不足,提出了一种基于核函数的局域线性自适应预测算法。该算法利用包含了相空间中邻近点之间的相对距离信息的核函数,将相空间中的邻近点投影到更高维的非线性核空间,在高维(甚至无穷维)的核空间用线性自适应算法预测混沌时间序列,相当于在原混沌相空间用高度非线性的函数预测高度非线性的混沌时间序列,可获得更好的预测结果。给出了应用该方法的具体步骤,通过仿真实验证明了该算法的有效性。     

混合自回归滑动平均模型———MARMA

提出一类新的用于非线性时间序列建模的混合自回归滑动平均模型(Mixture autoregressive movingaverage model简记MARMA).该模型条件分布富于变化的特点使得它能够描述非对称、多峰、以及条件异方差等非Gauss特征.研究得到了MARMA模型的平稳性条件和自相关函数.利用BIC(Bayes informationcriterion)准则来选择模型.运用EM(expectation maximization)算法估计模型的参数.将MARMA模型应用于一组金融数据,并和其它模型做比较.结果表明MARMA模型能够更准确地描述该数据的特征.     

股指期货仿真交易与现货相互引导关系

以沪深300股指期货仿真交易数据及沪深300指数为研究样本,在结合定性分析的同时,运用E-G协整检验、G ranger因果分析法对两个市场之间的长期均衡关系进行了研究,并通过使用向量自回归(VAR)模型、向量误差修正(VEC)模型、脉冲响应分析和方差分解等方法,进一步对变量间的相互引导关系进行了分析。结果表明仿真交易合约与HS300指数之间互为因果引导关系,且期货市场调整到均衡状态的速度要远快于现货市场。同时研究也发现,股指期货虽然在一定程度上对现货市场产生了一定的影响,在定价中也发挥了一定的作用,但是在价格发现方面总体来说受到现货市场的影响还是占相对主导地位,即我国仿真交易中的股指期货还没有达到成熟期货市场的价格发现水平。     

农村金融生态环境与金融效率相关性实证研究——基于典型相关分析与Granger因果检验

通过分别选取衡量农村金融生态环境和金融效率的指标,利用典型相关分析、Granger因果检验等统计方法,对1985～2008年农村金融生态环境和农村金融效率之间的相关性进行实证研究,结果表明二者之间存在显著的相关性,并反映出农村金融较脆弱的现状。以此为依据,提出改善农村金融生态环境、促进农村金融高效运行的政策建议。     

上海股市日内波动性与成交量之间引导关系的实证分析

采用上海股市 2 0 0 1年 1月 2日至 2 0 0 1年 6月 2 9日期间的每 5分钟交易数据 ,用引导关系检验法对上海股市日内波动性与成交量变动率的关系进行了实证分析 ,结果表明 ,它们之间存在双向线性引导关系 ,这意味着可以利用成交量变动率的线性模型来预测日内波动性 ;但它们之间不存在非线性引导关系 ,这意味着在预测上海股市日内波动性时 ,不能得出支持非线性预测模型的结论 .