共查询到20条相似文献,搜索用时 807 毫秒
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研究了标准均值方差投资组合选择模型,针对目前求解方法不具有多项式算法复杂性,文章给出了求解均值方差投资组合优化模型的原对偶内点算法.该算法具有多项式复杂性,因此可以快速求解大规模的投资组合优化模型.仿真结果表明,原对偶内点算法可以较好地应用于投资组合问题,具有较广泛的应用空间和一定的推广价值. 相似文献
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针对ADF和PP检验对含有均值结构变点时间序列的“伪检验”问题,文章基于贝叶斯理论,先运用贝叶斯因子模型选择的方法检测时序结构变点位置,再在结构变点已知的情况下运用置信区间和贝叶斯因子两种方法检验序列是否存在单位根,并用Monte Carlo模拟方法进行仿真,验证该方法的有效性。研究发现:是否考虑均值结构变点对时间序列的单位根检验有着重要的影响,不考虑结构突变而进行常规的单位根检验会产生误判;贝叶斯方法能够有效检测含有均值结构变点时间序列的变点位置,并能提高单位根检验功效。 相似文献
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文章提出了估计正态序列均值变点位置的非迭代抽样算法.利用逆贝叶斯公式,得到了变点位置的精确后验分布,通过对该离散分布抽取样本,得到变点位置的贝叶斯估计.模拟显示该算法能有效地估计变点位置,并且计算速度比迭代的Gibbs抽样算法快. 相似文献
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文章提出指数加权移动平均(EWMA)组合模型克服了传统组合方法没有考虑时序数据间时隔远近而相互影响不同的动态关联缺陷.对汇改后人民币汇率实证分析,结果发现EWMA组合模型比被组合的广义自回归条件异方差(GARCH)模型和均值回复(Mean Reversion)模型有更好的预测精度,能够更加逼真把握金融时序的未来走势. 相似文献
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文章基于递归残差的逆序特征和隔离检测研究了回归模型多参数变点的检测方法。首先,构建带有变点的回归模型,考虑到多元正向CUSUM检验能防止协变量均值与偏移量正交时损失功效,但其变点检测效果并不理想的情况,引入修正的检验统计量BCUSUM。其次,结合快速高效的隔离检测技术,提出MCPDP算法用于估计变点数目及位置。最后,模拟结果表明,所提出的方法能较好地控制检验水平,有更高的功效;评价结果显示,MCPDP算法在变点估计性能方面表现较优;实例分析表明,交通流变点符合实际交通情况,验证了该方法的有效性,且所构建的模型可以作为交通参数确定性经验关系的一种修正。 相似文献
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《统计与信息论坛》2019,(6):3-9
时间序列自回归AR模型的Yule-Walker估计法在建模过程中易受离群值的影响,导致计算结果与实际不相符。针对这一现象,基于均值和方差的稳健组合估计量构建了稳健自相关函数,得到了时序AR模型的稳健Yule-Walker估计算法,以克服离群值的影响。并对此方法进行了模拟与金融数据实证检验,模拟和实证检验均表明:当时序数据中不存在离群值时,传统估计方法与稳健估计方法得到的结果基本保持一致;当数据中存在离群值时,运用传统估计方法得到的结果出现较大变化,而运用稳健估计方法得到的结果基本不变。这说明相对于传统估计方法,稳健估计方法能有效抵抗离群值的影响,具有良好的抗干扰性和高抗差性。 相似文献
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《Journal of Statistical Computation and Simulation》2012,82(1):65-87
Procedures for detecting change points in sequences of correlated observations (e.g., time series) can help elucidate their complicated structure. Current literature on the detection of multiple change points emphasizes the analysis of sequences of independent random variables. We address the problem of an unknown number of variance changes in the presence of long-range dependence (e.g., long memory processes). Our results are also applicable to time series whose spectrum slowly varies across octave bands. An iterated cumulative sum of squares procedure is introduced in order to look at the multiscale stationarity of a time series; that is, the variance structure of the wavelet coefficients on a scale by scale basis. The discrete wavelet transform enables us to analyze a given time series on a series of physical scales. The result is a partitioning of the wavelet coefficients into locally stationary regions. Simulations are performed to validate the ability of this procedure to detect and locate multiple variance changes. A ‘time’ series of vertical ocean shear measurements is also analyzed, where a variety of nonstationary features are identified. 相似文献
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Giada Adelfio 《统计学通讯:模拟与计算》2013,42(4):437-448
A new approach based on the fit of a generalized linear regression model is introduced for detecting change-points in the variance of heteroscedastic Gaussian variables, with piecewise constant variance function. This approach overcome some limitations of both exact and approximate well-known methods that are based on successive application of search and tend to overestimate the real number of changes in the variance of the series. The proposed method just requires the computation of a gamma GLM with log-link, resulting in a very efficient algorithm even with large sample size and many change points to be estimated. 相似文献
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In this paper we analyse the performances of a novel approach to modelling non-linear conditionally heteroscedastic time series
characterised by asymmetries in both the conditional mean and variance. This is based on the combination of a TAR model for
the conditional mean with a Constrained Changing Parameters Volatility (CPV-C) model for the conditional variance. Empirical
results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes. 相似文献
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In this article we examine small sample properties of a generalized method of moments (GMM) estimation using Monte Carlo simulations. We assume that the generated time series describe the stochastic variance rate of a stock index. we use a mean reverting square-root process to simulate the dynamics of this instantaneous variance rate. The time series obtained are used to estimate the parameters of the assumed variance rate process by applying GMM. Our results are described and compared to estimates from empirical data which consist of volatility as well as daily volume data of the German stock market. One of our main findings is that estimates of the mean reverting parameter that are not significantly different from zero do not necessarily imply a rejection of the hypothesis of a mean reverting behavior of the underlying stochastic process. 相似文献
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Hao Jin Jinsuo Zhang Han Hao 《Journal of Statistical Computation and Simulation》2018,88(14):2651-2667
This paper considers the detection problem of variance changes for the time series involving abrupt and/or smooth breaks in mean. Often, in these situations, the tests of choice are based on cumulative sum of squares statistics. We show that the test statistics are not robust in the presence of broken mean and their sizes suffer severe distortions. The adjusted residual-based method is then proposed to eliminate these deficiencies and makes a significant improvement. Finally, simulation results confirm the validity of these modified test statistics, and an empirical data analysis using some stock price series from the Shanghai Stock Exchange is reported. 相似文献
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LENA R. OLSEN SIGRUNN H. SØRBYE FRED GODTLIEBSEN 《Scandinavian Journal of Statistics》2008,35(1):119-138
Abstract. The presented method called Significant Non-stationarities, represents an exploratory tool for identifying significant changes in the mean, the variance, and the first-lag autocorrelation coefficient of a time series. The changes are detected on different time scales. The statistical inference for each scale is based on accurate approximation of the probability distribution, using test statistics being ratios of quadratic forms. No assumptions concerning the autocovariance function of the time series are made as the dependence structure is estimated non-parametrically. The results of the analyses are summarized in significance maps showing at which time points and on which time scales significant changes in the parameters occur. The performance of the given method is thoroughly studied by simulations in terms of observed significance level and power. Several examples, including a real temperature data set, are studied. The examples illustrate that it is important to carry out the analysis on several time horizons. 相似文献
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Fernando Ferraz do Nascimento 《Journal of applied statistics》2017,44(13):2410-2426
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. 相似文献
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We investigate if portfolios can be improved if the classical Markowitz mean–variance portfolio theory is combined with recently proposed change point tests for dependence measures. Taking into account that the dependence structure of financial assets typically cannot be assumed to be constant over longer periods of time, we estimate the covariance matrix of the assets, which is used to construct global minimum-variance portfolios, by respecting potential change points. It is seen that a recently proposed test for changes in the whole covariance matrix is indeed partially useful whereas pairwise tests for variances and correlations are not suitable for these applications without further adjustments. 相似文献
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In this paper the class of Bilinear GARCH (BL-GARCH) models is proposed. BL-GARCH models allow to capture asymmetries in the
conditional variance of financial and economic time series by means of interactions between past shocks and volatilities.
The availability of likelihood based inference is an attractive feature of BL-GARCH models. Under the assumption of conditional
normality, the log-likelihood function can be maximized by means of an EM type algorithm. The main reason for using the EM
algorithm is that it allows to obtain parameter estimates which naturally guarantee the positive definiteness of the conditional
variance with no need for additional parameter constraints. We also derive a robust LM test statistic which can be used for
model identification. Finally, the effectiveness of BL-GARCH models in capturing asymmetric volatility patterns in financial
time series is assessed by means of an application to a time series of daily returns on the NASDAQ Composite stock market
index. 相似文献