股票市场价格与经济增长关系的实证分析

文章以上证综合指数、国内生产总值等宏观经济变量指标作为解释变量,采用2005年1月至2007年6月的月度数据,运用VAR模型、Granger因果关系检验、误差修正模型(VECM)等方法,对股票市场与宏观经济变量的关系进行了实证研究。     

非平稳条件下的Box Pierce Ljung预测检验理论及其应用

文章研究了非平稳对非平稳条件下的Box Pierce Ljung类检验的影响.在此基础上对传统的Box Pierce Ljung线性预测检验法进行了修正,并运用严格的理论证明和蒙特卡罗模拟证实了它们在大样本和有限样本情况下的非平稳稳健性.最后.运用修正的检验方法对上证综合指数的可预测性进行了分析.文章结论对于我国金融市场实证研究具有重要的指导意义,也反应了我国新兴金融市场的独有特性以及成熟统计方法在我国的实用性.     

上证180指数的贝叶斯估计

本文首先阐述了上证180指数及其现行算法,并给出了基于贝叶斯估计的算法,在此基础上将贝叶斯方法应用于上证180指数的编制中,并结合定基指数对此方法进行了实证研究。     

中国股票市场的量价关系研究

本文采用上证180指数的周数据验证我国股票市场上成交量同股价之间的granger因果关系.结果表明,在我国股票市场上,成交量对股价在一定时间的时滞上有很强的解释作用,而股价对成交量几乎没什么解释作用.     

关于股指期货交易中股价指数编制方法的讨论

我国的股票市场经过近十年的发展日渐成熟 ,对金融衍生工具的要求日益高涨 ,交易品种单一已成为制约我国股票市场进一步发展的障碍。各界人士对尽快开办我国的股票指数期货交易呼声很高 ,市场对此也有现实要求。开办股指期货交易涉及许多方面的问题 ,但合约标的设计是一个关键问题 ,一个合理的能代表股票市场总体变化的股价指数是股票指数交易成功的基本保证。本文只对股指期货交易合约的标的物——股价指数编制中的几个问题作些讨论。我国现有的股价指数主要有上交所编制的上证综合指数、上证 30指数和深交所编制的深圳综合指数、深圳成份…     

结构突变趋势平稳过程与随机趋势过程的虚假回归研究

 内容提要：在已有研究的基础上,本文更为深入的研究含有结构突变的趋势平稳变量与随机趋势变量间的虚假回归问题。本文推导出OLS估计下DW统计量、F统计量以及R2的极限分布,并且将回归模型扩展到动态情形下,推导出用于Granger因果检验的F统计量的极限分布;采用Monte Carlo模拟方法分析了数据生成过程的各项参数对各统计量有限样本分布的影响;最后,本文分析了在有限样本下,数据生成过程的各项参数对虚假回归及虚假Granger因果关系发生概率的影响。     

基于RS分析的中国债券市场分形特征研究

文章详细讨论了RS分析方法、概率空间分形维的测度,通过对上证国债指数和上证企债指数的RS分析,得到了两序列赫斯特指数和非周期循环长度,进一步得出中国债券市场为分形市场的结论.     

中国股市与国际股市的协整关系研究

股票市场是市场经济条件下最重要的资本市场。本文根据2005年～2008年期间中国股票市场与国际主要股票市场的每日收盘数据．运用单位根检验与协整检验分别对上海上证综合指数与香港国企H股指数、恒生指数、道琼斯指数和纳斯达克指数之间，深圳成份指数与香港国企H股指数、恒生指数、道琼斯指数和纳斯达克指数之间的是否存在协整关系进行了实证分析，从而研究我国股票市场是否与国际股票市场接轨。     

季节趋势平稳过程间的虚假回归

文章推导了当数据生成过程是独立的季节趋势平稳过程情形下,OLS参数估计及检验统计量的极限分布.由于序列中的趋势会导致虚假回归现象的发生.文章借助Monte Cado试验,对上述虚假回归中OLS统计量(t类统计量、R2、DW)的大样本渐近分布进行模拟,发现确实存在虚假回归现象并且受样本容量的影响不大.文章还针对我国数据样本期比较短的特点,就虚假回归下统计量的小样本(T=10,15,30,50)特征进行了模拟.     

ETF标的指数成分股流动性与市场隐性交易成本的实证分析

文章基于市场微观结构理论,采用贝叶斯Gibbs抽样测算方法,选取上证A股近10年的收盘价数据作为总体样本,以上证180ETF为研究标的,测算出上证180ETF上市前后5年上证180标的指数成分股和上证A股的隐性交易成本.以测算得出的隐性交易成本作为流动性指标,实证结果发现:在市场隐性交易成本上升的情况下,ETF上市相对提升了标的指数成分股的流动性;ETF上市对不同行业成分股的流动性影响不同,隐性交易成本波动性较稳定的行业流动性更高.     

趋势平稳过程与单位根过程的趋势特征研究

为了深入研究具有高次趋势特征序列的单位根(平稳性)检验问题,研究了高次趋势平稳过程和带高次趋势的单位根过程的概念及其时间趋势特征。结果表明,带漂移的单位根过程实际具有线性趋势,带k(k≥1)次趋势的单位根过程实际具有k+1次趋势;而k(k≥0)次(趋势)平稳过程则具有k次趋势。无论是趋势平稳过程,还是单位根过程,都可以通过差分变换确定其时间趋势特征。     

Mixing and moments properties of a non-stationary copula-based Markov process

Abstract

We provide conditions under which a non-stationary copula-based Markov process is geometric β-mixing and geometric ρ-mixing. Our results generalize some results of Beare who considers the stationary case. As a particular case we introduce a stochastic process, that we call convolution-based Markov process, whose construction is obtained by using the C-convolution operator which allows the increments to be dependent. Within this subclass of processes we characterize a modified version of the standard random walk where copulas and marginal distributions involved are in the same elliptical family. We study mixing and moments properties to identify the differences compared to the standard case.     

Self-weighted quasi-maximum exponential likelihood estimator for ARFIMA-GARCH models

In this paper, a local self-weighted quasi-maximum exponential likelihood estimator for ARFIMA-GARCH models is proposed, asymptotic normality of this estimator is derived under the existence of second moment including stationary and non-stationary cases. A simulation study is given to evaluate the performance of the proposed self-weighted QMELE under the stationary case.     

Testing for coefficient stability of AR(1) model when the null is an integrated or a stationary process

In this paper, we propose a new test for coefficient stability of an AR(1) model against the random coefficient autoregressive model of order 1 neither assuming a stationary nor a non-stationary process under the null hypothesis of a constant coefficient. The proposed test is obtained as a modification of the locally best invariant (LBI) test by Lee [(1998). Coefficient constancy test in a random coefficient autoregressive model. J. Statist. Plann. Inference 74, 93–101]. We examine finite sample properties of the proposed test by Monte Carlo experiments comparing with other existing tests, in particular, the LBI test by McCabe and Tremayne [(1995). Testing a time series for difference stationary. Ann. Statist. 23 (3), 1015–1028], which is for the null of a unit root process against the alternative of a stochastic unit root process.     

Modelling and Prediction of Financial Time Series

We consider statistical aspects of the modelling and prediction theory of time series in one and many dimensions. We discuss Lévy-based and general models, and the stationary and non-stationary cases. Our starting point is the recent pair of surveys, Szeg'ó's theorem and its probabilistic descendants and Multivariate prediction and matrix Szeg'ó theory, by this author.     

Testing for Bivariate Extreme Dependence Using Kendall's Process

Abstract. New tests for the hypothesis of bivariate extreme‐value dependence are proposed. All test statistics that are investigated are continuous functionals of either Kendall's process or its version with estimated parameters. The procedures considered are based on linear combinations of moments and on Cramér–von Mises distances. A suitably adapted version of the multiplier central limit theorem for Kendall's process enables the computation of asymptotically valid p‐values. The power of the tests is evaluated for small, moderate and large sample sizes, as well as asymptotically, under local alternatives. An illustration with a real data set is presented.     

A non-stationary integer-valued autoregressive model

It is frequent to encounter a time series of counts which are small in value and show a trend having relatively large fluctuation. To handle such a non-stationary integer-valued time series with a large dispersion, we introduce a new process called integer-valued autoregressive process of order p with signed binomial thinning (INARS(p)). This INARS(p) uniquely exists and is stationary under the same stationary condition as in the AR(p) process. We provide the properties of the INARS(p) as well as the asymptotic normality of the estimates of the model parameters. This new process includes previous integer-valued autoregressive processes as special cases. To preserve integer-valued nature of the INARS(p) and to avoid difficulty in deriving the distributional properties of the forecasts, we propose a bootstrap approach for deriving forecasts and confidence intervals. We apply the INARS(p) to the frequency of new patients diagnosed with acquired immunodeficiency syndrome (AIDS) in Baltimore, Maryland, U.S. during the period of 108 months from January 1993 to December 2001.     

Improved estimation for the autocovariances of a Gaussian stationary process

For a Gaussian stationary process with mean μ and autocovariance function γ(·), we consider to improve the usual sample autocovariances with respect to the mean squares error (MSE) loss. For the cases μ=0 and μ≠0, we propose sort of empirical Bayes type estimators Γ? and Γ?, respectively. Then their MSE improvements upon the usual sample autocovariances are evaluated in terms of the spectral density of the process. Concrete examples for them are provided. We observe that if the process is near to a unit root process the improvement becomes quite large. Thus, consideration for estimators of this type seems important in many fields, e.g., econometrics.     

Weak approximations for quantile processes of stationary sequences

ABSTRACT By studying the deviations between uniform empirical and quantile processes (the so-called Bahadur-Kiefer representations) of a stationary sequence in properly weighted sup-norm metrics, we find a general approach to obtaining weighted results for uniform quantile processes of stationary sequences. Consequently we are able to obtain weak convergence for weighted uniform quantile processes of stationary mixing and associated sequences. Further, by studying the sup-norm distance of a general quantile process from its corresponding uniform quantile process, we find that information at the two end points of the uniform quantile process can be so utilized that this weighted sup-norm distance converges in probability to zero under the so-called Csörgõ-Révész conditions. This enables us to obtain weak convergence for weighted general quantile processes of stationary mixing and associated sequences.     

Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling

We propose a new class of time dependent random probability measures and show how this can be used for Bayesian nonparametric inference in continuous time. By means of a nonparametric hierarchical model we define a random process with geometric stick-breaking representation and dependence structure induced via a one dimensional diffusion process of Wright-Fisher type. The sequence is shown to be a strongly stationary measure-valued process with continuous sample paths which, despite the simplicity of the weights structure, can be used for inferential purposes on the trajectory of a discretely observed continuous-time phenomenon. A simple estimation procedure is presented and illustrated with simulated and real financial data.