经济增长、股票收益率与股市流动性 之间的时变影响 ——基于TVP-SV-SVAR 模型的分析

中国股市流动性对提高股票收益率,促进经济发展的作用不容忽视,股票收益率与经济增长对 股市流动性的影响也在日益增强。将经济增长、股票收益率和股市流动性同时纳入TVP-SV-SVAR 分析 框架中,从时变视角分析三者之间的内在关系。实证结果表明,股票收益率是股市流动性和经济增长的 单向Granger 因果关系,股市流动性对股票收益率具有较强的时滞效应且时变性非常显著,经济增长对股 市流动性的冲击效应呈现“勺子”形态,而股市流动性对经济增长的冲击效应呈现“山谷”形态,不同时点 的股市流动性对经济增长的影响具有明显的不对称性和时变性。据此,建议从促进经济高质量增长、完 善股票市场制度、建立风险对冲机制等方面保持股市充足的流动性,以提高股票市场稳定性。     

Testing for structural changes in linear regressions with time-varying variance

Abstract

This article proposes a nonparametric test for structural changes in linear regression models that allows for serial correlation, autoregressive conditional heteroskedasticity and time-varying variance in error terms. The test requires no trimming of the boundary region near the end points of the sample period, and requires no prior information on the alternative, what it requires is the transformed OLS residuals under the null hypothesis. We show that the test has a limiting standard normal distribution under the null hypothesis, and is powerful against single break, multiple breaks and smooth structural changes. The Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes.     

Joint mean–covariance model in generalized partially linear varying coefficient models for longitudinal data

In longitudinal data analysis, efficient estimation of regression coefficients requires a correct specification of certain covariance structure, and efficient estimation of covariance matrix requires a correct specification of mean regression model. In this article, we propose a general semiparametric model for the mean and the covariance simultaneously using the modified Cholesky decomposition. A regression spline-based approach within the framework of generalized estimating equations is proposed to estimate the parameters in the mean and the covariance. Under regularity conditions, asymptotic properties of the resulting estimators are established. Extensive simulation is conducted to investigate the performance of the proposed estimator and in the end a real data set is analysed using the proposed approach.     

General saddlepoint approximations to distributions under an elliptical population

General saddlepoint approximations are derived for the distributions of statistics under an elliptical population. The technique is applied to obtain the tail probabilities of latent roots of a sample covariance matrix. It is shown that the method based on normalizing transformations by Tsuchiya and Konishi (1997) is efficient for the sample correlation coefficient in an elliptical sample.     

Dynamic Equicorrelation

A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, involves first adjusting for individual volatilities and then estimating correlations. A quasi-maximum likelihood result shows that DECO provides consistent parameter estimates even when the equicorrelation assumption is violated. We demonstrate how to generalize DECO to block equicorrelation structures. DECO estimates for U.S. stock return data show that (block) equicorrelated models can provide a better fit of the data than DCC. Using out-of-sample forecasts, DECO and Block DECO are shown to improve portfolio selection compared to an unrestricted dynamic correlation structure.     

TH-PPM-UWB系统在时变信道环境中的性能分析

分析了时变信道环境条件下TH-PPM-UWB系统的性能,并通过计算机仿真研究了室内信道环境中,散射体移动对不同参数条件下系统性能的影响。仿真结果表明在系统输入信噪比较小时,散射体的移动对系统性能的影响较小;随着输入信噪比的增加,散射体移动对系统性能的影响明显加剧。总体上,散射体移动对系统性能造成的影响相对与其他干扰来说并不明显,并且在视距及非视距环境中得到的结论基本相同。     

On the invertibility conditions for moving average processes

Conditions for the general Moving Average process, of order q, to be invertible or borderline non-invertible are deduced. These are termed the acceptability conditions. It turns out that they depend on the magnitude of the final moving average parameter, θ _q . If ‖θ _q ‖ >1, the process is not acceptable. Should ‖θ _q ‖ = 1, the conditions, for any particular q, follow simply - if use is made of the remainder theorem. When ‖θ_q‖< 1, an appeal is made to ROUCH* E'S theorem, to establish the conditions. Analogous stationarity results immediately follow for autoregressive processes.     

A New Chart for Monitoring the Covariance Matrix of Bivariate Processes

In this article we consider a control chart based on the sample variances of two quality characteristics. The points plotted on the chart correspond to the maximum value of these two statistics. The main reason to consider the proposed chart instead of the generalized variance | S | chart is its better diagnostic feature, that is, with the new chart it is easier to relate an out-of-control signal to the variables whose parameters have moved away from their in-control values. We study the control chart efficiency considering different shifts in the covariance matrix. In this way, we obtain the average run length (ARL) that measures the effectiveness of a control chart in detecting process shifts. The proposed chart always detects process disturbances faster than the generalized variance | S | chart. The same is observed when the size of the samples is variable, except in a few cases in which the size of the samples switches between small size and very large size.     

Rank-Based Classification Using Robust Discriminant Functions

The Pareto distribution model assumption in the peaks over threshold method, will be tested by making using of the Kolmogorov–Smirnov goodness of fit method. Pareto distributed variables can be transformed to exponential, and the test will be for exponentiality. It was found that the statistic can be used as an indication of where to choose the threshold and to check the Pareto model assumption.     

Posterior moments of scalar functions of a covariance matrix

Given multivariate normal data and a certain spherically invariant prior distribution on the covariance matrix, it is desired to estimate the moments of the posterior marginal distributions of some scalar functions of the covariance matrix by importance sampling. To this end a family of distributions is defined on the group of orthogonal matrices and a procedure is proposed for selecting one of these distributions for use as a weighting distribution in the importance sampling process. In an example estimates are calculated for the posterior mean and variance of each element in the covariance matrix expressed in the original coordinates, for the posterior mean of each element in the correlation matrix expressed in the original coordinates, and for the posterior mean of each element in the covariance matrix expressed in the coordinates of the principal variables.