Latent ancestral graph of structure vector autoregressive models

A class of latent ancestral graph for modelling the dependence structure of structural vector autoregressive (VAR) model affected by latent variables is proposed.The graphs are mixed graphs with possibly two kind of edges,namely directed and bidirected edges.The vertex set denotes random variables at difforent times.In Gaussian case,the latent ancestral graph leads to a simple parameterization model.A modified iterative conditional fitting algorithm is presented to obtain maximum likelihood estimation of the parameters.Furthermore,a log-likelihood criterion is used to select the most appropriate models.Simulations are performed using illustrative examples and results are provided to demonstrate the validity of the methods.     

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.     

非线性时间序列建模的混合GARCH方法

在文献[1]的基础上，首次提出混合广义自回归务件异方差（Mixture Generalized Autoregressive Conditional Heteroscedastic Model简记MGARCH）模型；给出并证明了MGARCH模型的一阶平稳性的充分必要条件及二阶平稳性的充分务件；给出该模型参数估计的EM算法：利用BIC定阶准则对MGARCH模型的各成份进行定阶；计算结果表明该模型对金融非线性时间序列中存在的变异率现象具有较强的描述能力，有广阔的应用前景。     

基于库存水平的ARMA(1 ,1) 需求条件下目标库存水平优化方法

为降低企业库存成本,考虑ARMA(1,1)过程且受到库存水平影响的需求情形,建立定期库存策略的目标库存水平优化模型,提出该模型的求解算法,最后结合数例验证模型的实用性和可操作性,并分析需求自相关程度和需求对库存水平的依赖度、进货提前期等因素对目标库存量的影响,得出:目标库存量随进货提前期和需求自相关程度的提高而上升,若需求自相关程度越大,目标库存量提高得越快;若需求与库存量之间呈现多项式函数关系,目标库存量随需求对库存的依赖度提高而上升,且上升速度逐渐加快;若需求与库存量之间呈指数函数关系,目标库存水平随需求对库存的依赖度提高而基本不变.当需求对库存的依赖度较小时,目标库存水平呈现很小的下降趋势,反之,基本不变.     

ARMA供应链模型研究

在基于ARMA时间序列的需求和目标库存最大策略的假定条件下,建立了供应链系统模型。利用时间序列分析方法推导了库存序列、订货序列和库存残差序列的方程表达式,证明库存序列、订货序列和库存残差序列同样为ARMA时间序列,自回归和移动平均的阶数依赖于需求时间序列的阶数和提前期的大小,且订货序列和库存残差序列的自回归部分与需求的自回归部分相同。     

滑动窗口二次自回归模型预测混沌时间序列

提出一种新颖的非线性时间序列预测模型,即滑动窗口二次自回归(MWDAR)模型.MWDAR模型使用部分的历史数据及其二次项构造自回归模型.模型参数用线性最小二乘法估计.应用模型进行预测时,预先选定窗口大小以及模型一次项和二次项的阶次.在每个当前时刻,先根据窗口内的数据估计模型参数,然后根据输入向量及模型参数做出预测.这种预测方法不仅适合小数据集的时间序列预测,而且对大数据集具有极高的计算效率.分别用Henon混沌时间序列数据和真实的股票交易数据作了MWDAR方法与局域线性化方法的1步和多步预测对比实验.结果显示MWDAR方法无论在预测精度上,还是在计算效率上都优于局域线性化方法.     

半参数STAR模型的估计及应用

文中首次提出了一个新的STAR模型,在保留了转换函数的前提下,让转换变量以非参数的形式进入转换函数,从而有效减少了模型误设的风险,提高了样本内拟合和样本外预测的能力. 蒙特卡罗实验的结果显示半参数STAR模型的有限样本拟合结果令人满意. 利用1994年1月到2012年7月的人民币实际有效汇率月度数据,将半参数STAR模型和随机游走模型、自回归模型、门限自回归模型、平滑转换自回归模型和人工神经网络模型的样本外预测能力进行比较,结果显示半参数STAR模型在样本外预测能力上具有显著优势.     

Parsimonious Mean-Covariance Modeling for Longitudinal Data with ARMA Errors

Based on the generalized estimating equation approach, the authors propose a parsimonious mean-covariance model for longitudinal data with autoregressive and moving average error process, which not only unites the existing autoregressive Cholesky factor model and moving average Cholesky factor model but also provides a wide variety of structures of covariance matrix. The resulting estimators for the regression coefficients in both the mean and the covariance are shown to be consistent and asymptotically normally distributed under mild conditions. The authors demonstrate the effectiveness, parsimoniousness and desirable performance of the proposed approach by analyzing the CD4+ cell counts data set and conducting extensive simulations.     

Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model

Journal of Systems Science and Complexity - In this paper empirical likelihood (EL)-based inference for a semiparametric varying-coefficient spatial autoregressive model is investigated. The...     

Identification of Subset Autoregressive Model Using Simulaated Annealing

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A Study on the Volatility of the Bangladesh Stock Market——Based on GARCH Type Models

The generalized autoregressive conditional heteroskedasticity(GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant "low-yield associated with high-risk" phenomenon is detected in the crisis period and the "leverage effect" occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity(TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.     

非局部正则化的压缩感知图像重建算法

压缩感知(compressed sensing, CS)图像重建算法是CS图像获取问题的一个研究重点。针对传统基于稀疏性先验的重建算法不能有效重建图像的各种结构特征,为了在测量值数量不变的情况下进一步提高图像的重建质量,在稀疏性先验的基础上,引入局部自回归模型和非局部自相似性作为图像额外的先验信息,建立了非局部正则化的CS图像重建模型,并给出了相应的数值求解算法。此外,对于重建模型中图像的自回归参数,给出一种基于非局部相似点的估计方法。实验结果表明,较之传统的稀疏性正则化重建算法和同类的MARX(model based adaptive recovery of compressive sensing)算法,所提算法能获得更高的图像重建质量。     

三阶段均值回复、TAR及其应用

本文首次定义了三阶段均值回复过程, 其用于刻画一类特殊的均值回复现象, 并可用于解释非线性均值回复现象和时间序列短期的"不平稳"现象. 门限自回归模型(threshold autoregressive model, TAR)和机制转换模型(regime-switching model)可用于三阶段均值回复过程的建模. 作为实证例子, 本文使用三阶段门限自回归模型拟合了我国对美国和香港的贸易顺差的对数增长率. 实证研究发现: 近十年来, 该增长率处于高水平均值过程(扩张期)的概率均高于50%; 收缩期的平均增长率水平最低, 而扩张期的平均增长率水平最高; 各均值过程的均值回复特征表现为低水平的均值方程的常数项相对较高, 但斜率系数相对较低, 而高水平的均值方程常数项相对较低, 但斜率系数相对较高. 因此, 可以使用三阶段TAR模型来建模三阶段均值回复现象.     

海面小目标检测的AR双极点分析

针对舰载(或岸基)雷达对海面小目标检测问题,尤其对较短时间内完成检测的应用场合,基于自回归(AR)模型的解决方案,对其中具有代表性的算法———最大幅度极点法(ARLPM)的不足进行了详细分析,提出同时利用次大幅度极点信息的AR双极点分析法。给出了应用该方法的具体步骤,雷达数据检测结果证明该方法优于ARLPM法。     

A new approach to model financial markets

This paper deals with the problem of how to take full use of prices information to model financial markets. A range decomposition technique is proposed to decompose the returns into two components. It is proved theoretically that these two components are bi-directional Granger causality, which makes it convenient to establish a vector autoregressive model (VAR). Both simulations and empirical studies are performed, and the results are consistent with the theoretical ones. The range decomposition approach presented in this paper is more efficient in information employment and suggests a new framework to model financial markets.     

基于已实现二阶矩预测的期货套期保值策略及对股指期货的应用

资产收益的跳跃行为给套期保值决策带来了挑战. 提出了考虑跳跃、基于预测的VecHAR-RVRCOV-J模型, 首次将高频数据中蕴含的跳跃信息引入套期保值决策, 对期货和现货收益率的已实现二阶矩做异质滞后阶向量自回归, 构造动态套期保值比率的预测统计量. 实证应用中以沪深300股指期货及沪深300指数为对象构建套期保值策略, 在样本内和样本外的综合套保绩效考核上, 新模型优于常用的二元GARCH模型.     

A multiscale modeling approach incorporating ARIMA and anns for financial market volatility forecasting

The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.     

短期负荷预报模型库的研究及应用

本文针对电力负荷变化的非平稳性和周期性，采用灰色模型，可调灰色模型分析用电负荷的趋势项并与历史负荷比较得一系列残差，然后应用自回归模型，傅氏模型，人工神经网络模型进行修正以提高精度。用一系列组合模型分别用于不同场合和要求下的负荷预测，并在微机上开发软件，通过实例计算，效果良好，具有一定的应用价值.     

战斗部虚拟试验多级模型集成方法研究

弹药虚拟试验预测模型建模过程需多次调用数值仿真模型,对于运行耗时的大型三维计算模型将面临严重的计算复杂性问题.当计算模型存在多种精度时,提出一种基于自回归模型的预测模型建模方法,可集成多种精度的计算模型,以较少高精度模型和较多低精度模型分别获取训练样本,建立效率与精度平衡的贝叶斯预测模型.设计某型中小口径穿甲弹穿甲威力虚拟试验算例验证该方法的高效性.     

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.