Exploring the Impact of Multivariate GARCH Innovations on Hypothesis Testing for Cointegrating Vectors

This article investigates the impact of multivariate generalized autoregressive conditional heteroskedastic (GARCH) errors on hypothesis testing for cointegrating vectors. The study reviews a cointegrated vector autoregressive model incorporating multivariate GARCH innovations and a regularity condition required for valid asymptotic inferences. Monte Carlo experiments are then conducted on a test statistic for a hypothesis on the cointegrating vectors. The experiments demonstrate that the regularity condition plays a critical role in rendering the hypothesis testing operational. It is also shown that Bartlett-type correction and wild bootstrap are useful in improving the small-sample size and power performance of the test statistic of interest.     

LONG-RANGE DEPENDENT COMMON FACTOR MODELS: A BAYESIAN APPROACH

We propose a simulation-based Bayesian approach to analyze multivariate time series with possible common long-range dependent factors. A state-space approach is used to represent the likelihood function in a tractable manner. The approach taken here allows for extension to fit a non-Gaussian multivariate stochastic volatility (MVSV) model with common long-range dependent components. The method is illustrated for a set of stock returns for companies having similar annual sales.     

A New Spatial Skew-Normal Random Field Model

Skewness is often present in a wide range of geostatistical problems, and modeling it in the spatial context remains a challenging problem. In this article, we propose and study a new class of spatial skew-normal random fields, defined in terms of the closed multivariate skew-normal distribution. Such fields can be written as the sum of two independent fields: one Gaussian and the other truncated Gaussian. We derive theoretical expressions for the first- and second-order moments, and use them within a method of moments based procedure to estimate the parameters of the model. Data simulated from the model are used to illustrate the methodology developed.     

Probability-Based Process Capability Indices

This article deals with alternative process capability indices (PCIs) to traditional basic PCIs C _p , C _pk , and C _pm based on different fraction conforming type of probabilities. In view of various problems of constructing capability indices for univariate as well as multivariate set up, these alternative PCIs are very useful as compared to C _p , C _pk , and C _pm . Computing aspects of proposed PCIs are discussed for normal and non normal processes when process tolerance is symmetric as well as asymmetric. Generalization of these PCIs for multivariate set up is also discussed. Some simulation study results and real life problems are given for applications of proposed PCIs.     

A Stochastic Volatility Model With Markov Switching

This article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and the medium-volatility states. The high-volatility regime is able to capture the 1987 crash and overlap considerably with four U.S. economic recession periods.     

Further results on multivariate vertical density representation and an application to random vector generation *

In this paper we further develop the theory of vertical density representation (VDR) in the multivariate case and provide a formula for the calculation of the conditional probability density of a random vector when its density value is given. An application to random vector generation is also given.     

On improvement of prediction in arma processes

Necessary and sufficient conditions are derived in the paper that enable to decide whether an additional multivariate process will improve the prediction in a given multivariate discrete stationary process. The both processes are assumed to form together a process ARMAm n Further it was investigated wnen one can asser t that the both processes are uncorrelated provided the additional process did not improve the prediction in the original process, Some hints for the actual construction of predictors in a multivariate ARMA. (m n) process can be found in the paper.     

Hotelling’s T2 control chart with double warning lines

Recent studies have shown that the T ² control chart with variable sampling intervals (VSI) and/or variable sample sizes (VSS) detects process shifts faster than the traditional T ² chart. This article extends these studies for processes that are monitored with VSI and VSS using double warning lines (T ² —DWL). It is assumed that the length of time the process remains in control has exponential distribution. The properties of T ² —DWL chart are obtained using Markov chains. The results show that the T ² —DWL chart is quicker than VSI and/or VSS charts in detecting almost all shifts in the process mean.     

High dimensional multivariate mixed models for binary questionnaire data

Summary.  Questionnaires that are used to measure the effect of an intervention often consist of different sets of items, each set possibly measuring another concept. Mixed models with set-specific random effects are a flexible tool to model the different sets of items jointly. However, computational problems typically arise as the number of sets increases. This is especially true when the random-effects distribution cannot be integrated out analytically, as with mixed models for binary data. A pairwise modelling strategy, in which all possible bivariate mixed models are fitted and where inference follows from pseudolikelihood theory, has been proposed as a solution. This approach has been applied to assess the effect of physical activity on psychocognitive functioning, the latter measured by a battery of questionnaires.     

人民币汇率与股价的ARCH效应检验及模型分析

随着人民币汇率弹性的加大,汇率与股价的关联效应开始显现出来,有可能导致外汇市场、股票市场乃至整个金融市场的紊乱,所以两者的关联研究对于整个金融市场的安全与发展具有较大的现实意义。通过对人民币各种汇率与股价的逐日数据所作的ARCH效应检验,得出相应的GARCH和EGARCH模型,并证明人民币币值与股价呈反向关系。