股指期货预测模型构建及其应用效果分析

文章选择股指期货价格以及基差预测这一理论界和实务界共同关心的问题为研究对象,使用香港恒生期货数据为样本,分别采用时间序列ARIMA、ARMA模型对恒生期货连续指数的日收盘价对数序列LHF和基差序列BASIS进行建模分析,并利用预测误差检验量对模型样本外的预测效果进行了实证研究.结果表明,ARIMA(3,1,3)模型很好地拟合和预测了股指期货指数对数LHF序列的走势,达到了预测目的;ARMA(1,1)和ARMA(3,3)模型在预测精度方面不甚理想但基本刻画了基差序列的变动趋势.     

中国银行间回购利率已实现跳跃的甄别与建模

以二次幂变差的测量为理论基础,研究了银行间回购利率的跳跃行为.将已实现波动率分解为连续样本路径方差和离散跳跃方差,研究了跳跃方差序列的统计特征,并且应用HAR-RV-CJ模型对中国银行问回购利率的已实现波动率进行预测.     

多种单位根检验方法的比较——以税收时间序列为例

文章利用常规单位根检验法和含结构突变点单位根检验法对改革开放以后的税收数据进行了平稳性检验.在ADF、PP、KPSS、NP检验方法下,我国税收是Ⅰ(1)过程;若假设税收数据只含有一个突变点,则不管突变点已知还是未知,税收都是结构突变的单位根过程;若假设税收数据含有两个突变点,则税收是结构突变的趋势平稳过程.因而文章得出结论:从长期来看,具有改革意义的税收政策会改变税收时间序列原有路径,从不同程度上推动税收总量的增加及税收增长速度的提高;但短期内,数据生成过程不变.     

2005年汇改后人民币汇率多次结构突变检验分析——基于循序检验方法

采用最新的多次结构突变循序检验方法,对2005年7月21日汇改后人民币汇率时间序列趋势项是否具有多次结构突变进行研究,并在多次结构突变检验结果的基础上对消除趋势后的人民币汇率数据进行分析,结果发现：人民币汇率时间序列是围绕着4个结构断点的分段趋势平稳的;人民币汇率服从分段趋势平稳的结论对汇率政策有效性、汇率与其他经济总量关系研究及汇率预测具有重要意义。     

考虑记忆性质与时间滞后效应的非线性经济周期模型分析

考虑非线性经济周期模型中经济变量存在记忆性质与时间滞后现象,研究随机周期作用激励下Goodwin模型的随机响应,以此研究记忆性质与时间滞后现象对经济周期波动的具体影响。通过随机多尺度方法得到了模型的确定性与随机情形下的稳态响应。结果发现:当考虑非线性投资函数时,经济变量的时间记忆性质和时间滞后现象均可以导致经济波动方式的改变;当考虑非线性消费函数时,经济变量的时间记忆性质与时间滞后现象均可以诱导出经济周期波动的随机跳跃现象,即引发经济系统的突变。同时,随机周期作用也可以诱发系统出现稳态概率密度函数的分岔现象出现,说明外部随机周期作用可以诱发经济系统的突变现象产生。     

含有变结构点的面板循序检验方法研究

针对含有变结构点的面板数据易产生"伪单位根"现象,提出面板循序检验方法:首先给出检验模型和检验步骤,其次通过Monte Carlo模拟得到检验统计量的临界值,最后结合我国各地区的GDP数据进行实证分析.研究发现:中国GDP数据为带有结构突变的趋势平稳序列.     

“堤坝”型结构突变的时间序列单位根检验及其应用——对PPP的经验分析

通过解读一些经济变量的动态行为,提出了＂堤坝＂型确定趋势的概念和＂堤坝＂型结构突变的时间序列单位根检验。在结构突变位置未知的情况下,讨论了推断结构突变点位置的方法,给出了＂堤坝＂型结构突变单位根检验的一般步骤。最后,基于人民币和日元兑换美元的汇率数据,借助该检验为购买力平价（PPP）理论提供了有力的证据。     

郑州期货市场量价关系的实证分析

一、研究方法和模型选择(一)交易量处理本文采集了2004年6月1日至2006年7月28日棉花期货合约每天的收盘价和交易量(数据来源:郑州商品交易所网站)。由于每个期货合约都将在一定时间到期,因此如何产生一个连续的期货价格序列是个难题。本文选取离交割期最近月份的期货合约作为代表,在进入交割月后选取下一个最靠近交割月份的合约,得到连续期货价格序列和交易量序列。原始的交易量数据存在着非平稳性和时间序列相关性问题,因此需要用下面的自回归模型ARMA(p,q)对交易量数据进行处理,以得到一个平稳的、非相关的交易量序列作为信息指标的代理:     

时间序列分析的AR模型在全社会消费品零售总额预测中的应用

全社会消费品零售总额是一个与宏观经济运行状况有重要关系的经济变量,本文对全社会消费品零售总额作时间序列分析,建立AR模型进行趋势预测.我们以1987年至1995年的统计数据,作为时间序列分析的样本观测值,数据单位为人民币亿元,样本长度n=18.首先,假定全社会消费品零售总额x_t是一个平稳时间序列,为选择适当的描述x_t增长与变化规律的数字模型,我们对样本观测值进行数据分析,由公式     

关于我国上证指数突变点的研究

文章引入了对突变点检验的贝叶斯推断方法,介绍了该方法在指数族分布尤其是正态分布下突变点检验的基本理论,并应用该理论研究了我国上证指数的突变点问题.结果表明,上证指数序列在2005年11月附近存在突变点;加入突变点后,上证指教序列不再是单位根过程,而是趋势平稳过程.     

Bayesian Estimation of the Change Point Using X¯ Control Chart

The process personnel always seek the opportunity to improve the processes. One of the essential steps for process improvement is to quickly recognize the starting time or the change point of a process disturbance. The proposed approach combines the X¯ control chart with the Bayesian estimation technique. We show that the control chart has some information about the change point and this information can be used to make an informative prior. Then two Bayes estimators corresponding to the informative and a non informative prior along with MLE are considered. Their efficiencies are compared through a series of simulations. The results show that the Bayes estimator with the informative prior is more accurate and more precise when the means of the process before and after the change point time are not too closed. In addition, the efficiency of the Bayes estimator with the informative prior increases as the change point goes away from the origin.     

航班动态定价机制下的机票价格序列变点估计

文章旨在研究受航空业动态定价机制影响下的机票价格序列变点估计模型,文中分析了机票价格u8序列数据的结构特点,提出了可用于高噪声数据环境下、阶梯状、带明显多变点的多阶段序列变点估计框架,该框架中级联组合了DBSCAN算法、EM-高斯混合模型聚类、凝聚层次聚类算法和基于乘积划分模型的变点估计方法等多种成熟的数据分析方法,通过对“北京-昆明”航线航班的实证分析,验证了数据分析框架的有效性和普遍适用性。     

Optimal Detection of a Change Point in a Poisson Process for Different Observation Schemes

Abstract.  Change point problems are considered where at some unobservable time the intensity of a point process ( T_n ), n ∈  N , has a jump. For a given reward functional we detect the change point optimally for different information schemes. These schemes differ in the available information. We consider three information levels, namely sequential observation of ( T_n ), ex post decision after observing the point process up to a fixed time t * and a combination of both observation schemes. In all of these cases the detection problem is viewed as an optimal stopping problem which can be solved by deriving a semimartingale representation of the gain process and applying tools from filtering theory.     

中国GDP数据周期性的时间序列分析——基于制度及其变迁的一个解释

文章利用时间序列模型等计量方法,对中国GDP总量序列进行了时间趋势分解并结合实证结果对中国经济的发展周期进行了重新的划分.同时以此为视角,进一步提出了我国经济周期波动的特点;中国经济周期的超制度特征以及转轨期经济波动的新规律.     

Algorithms for Point Processes Analysis

A time point process can be defined either by the statistical properties of the time intervals between successive points or by those of the number of points in arbitrary time intervals. There are mathematical expressions to link up these two points of view, but they are in many cases too complicated to be used in practice. In this article, we present an algorithmic procedure to obtain the number of points of a stationary point process recorded in some time intervals by processing the values of the distances between successive points. We present some results concerning the statistical analysis of these numbers of points and when analytical calculations are possible the experimental results obtained with our algorithms are in excellent agreement with those predicted by the theory. Some properties of point processes in which theoretical calculations are almost impossible are also presented.     

Non‐parametric Bayesian Intensity Model: Exploring Time‐to‐Event Data on Two Time Scales

Time‐to‐event data have been extensively studied in many areas. Although multiple time scales are often observed, commonly used methods are based on a single time scale. Analysing time‐to‐event data on two time scales can offer a more extensive insight into the phenomenon. We introduce a non‐parametric Bayesian intensity model to analyse two‐dimensional point process on Lexis diagrams. After a simple discretization of the two‐dimensional process, we model the intensity by a one‐dimensional piecewise constant hazard functions parametrized by the change points and corresponding hazard levels. Our prior distribution incorporates a built‐in smoothing feature in two dimensions. We implement posterior simulation using the reversible jump Metropolis–Hastings algorithm and demonstrate the applicability of the method using both simulated and empirical survival data. Our approach outperforms commonly applied models by borrowing strength in two dimensions.     

A Bayesian model for multiple change point to extremes,with application to environmental and financial data

Abrupt changes often occur for environmental and financial time series. Most often, these changes are due to human intervention. Change point analysis is a statistical tool used to analyze sudden changes in observations along the time series. In this paper, we propose a Bayesian model for extreme values for environmental and economic datasets that present a typical change point behavior. The model proposed in this paper addresses the situation in which more than one change point can occur in a time series. By analyzing maxima, the distribution of each regime is a generalized extreme value distribution. In this model, the change points are unknown and considered parameters to be estimated. Simulations of extremes with two change points showed that the proposed algorithm can recover the true values of the parameters, in addition to detecting the true change points in different configurations. Also, the number of change points was a problem to be considered, and the Bayesian estimation can correctly identify the correct number of change points for each application. Environmental and financial data were analyzed and results showed the importance of considering the change point in the data and revealed that this change of regime brought about an increase in the return levels, increasing the number of floods in cities around the rivers. Stock market levels showed the necessity of a model with three different regimes.     

A permutation test for umbrella alternatives

There is a wide variety of stochastic ordering problems where K groups (typically ordered with respect to time) are observed along with a (continuous) response. The interest of the study may be on finding the change-point group, i.e. the group where an inversion of trend of the variable under study is observed. A change point is not merely a maximum (or a minimum) of the time-series function, but a further requirement is that the trend of the time-series is monotonically increasing before that point, and monotonically decreasing afterwards. A suitable solution can be provided within a conditional approach, i.e. by considering some suitable nonparametric combination of dependent tests for simple stochastic ordering problems. The proposed procedure is very flexible and can be extended to trend and/or repeated measure problems. Some comparisons through simulations and examples with the well known Mack & Wolfe test for umbrella alternative and with Page’s test for trend problems with correlated data are investigated.     

On- and offline detection of structural breaks in thermal spraying processes

We investigate and develop methods for structural break detection, considering time series from thermal spraying process monitoring. Since engineers induce technical malfunctions during the processes, the time series exhibit structural breaks at known time points, giving us valuable information to conduct the investigations. First, we consider a recently developed robust online (also real-time) filtering (i.e. smoothing) procedure that comprises a test for local linearity. This test rejects when jumps and trend changes are present, so that it can also be useful to detect such structural breaks online. Second, based on the filtering procedure we develop a robust method for the online detection of ongoing trends. We investigate these two methods as to the online detection of structural breaks by simulations and applications to the time series from the manipulated spraying processes. Third, we consider a recently developed fluctuation test for constant variances that can be applied offline, i.e. after the whole time series has been observed, to control the spraying results. Since this test is not reliable when jumps are present in the time series, we suggest data transformation based on filtering and demonstrate that this transformation makes the test applicable.     

Stochastic comparison of processes generated by random interruptions of monotone functions and related results

Many stochastic processes considered in applied probability models, and, in particular, in reliability theory, are processes of the following form: Shocks occur according to some point process, and each shock causes the process to have a random jump. Between shocks the process increases or decreases in some deterministic fashion. In this paper we study processes for which the rate of increase or decrease between shocks depends only on the height of the process. For such processes we find conditions under which the processes can be stochastically compared. We also study hybrid processes in which periods of increase and periods of decrease alternate. A further result yields a stochastic comparison of processes that start with a random jump, rather than processes in which there is at the beginning some random delay time before the first jump.Supported by NSF Grant DMS 9303891.