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Monitoring cross-sectional and serially interdependent processes has become a new issue in statistical process control (SPC). In up-to-date SPC literature, Kalman filtering was reported to monitor univariate autocorrelated processes. This paper applies a Kalman filter or state-space method for SPC to monitoring multivariate time series. We use Aoki's approach to estimate the parameter matrices of a state-space model. Multivariate Hotelling T2 control charts are employed to monitor the residuals of the state-space. Examples of this approach are illustrated.Quality Control Charts, Spc, State-space, Multivariate Time Series, Aoki's Approach,

In this study, we demonstrate the usefulness of ARIMA-Intervention time series analysis as both an analytical and forecast tool. The data base for this study is from the PACAP-CCER China Database developed by the Pacific-Basin Capital Markets (PACAP) Research Center at the University of Rhode Island (USA) and the SINOFIN Information Service Inc, affiliated with the China Center for Economic Research (CCER) of Peking University (China). These data are recent and not fully explored in any published study. The forecasting analysis indicates the usefulness of the developed model in explaining the rapid decline in the values of the price index of Shanghai A shares during the world economic debacle stating in China in 2008. Explanation of the fit of the model is described using the latest development in statistical validation methods. We note that the use of a simpler technique although parsimonious will not explain the variation properly in predicting daily Chinese stock prices. Furthermore, we infer that the daily stock price index contains an autoregressive component; hence, one can predict stock returns.

Abstract. In this article we test the random walk hypothesis in the German daily stock prices by means of a unit root test and the development of an ARIMA model for prediction. The results show that the time series of daily stock returns for a stratifi ed random sample of German fi rms listed on the stock exchange of Frankfurt exhibit unit roots. Also, we fi nd that one may predict changes in the returns to these listed stocks. These time series exhibit properties which are forecast able and provide the intelligent data analysts' methods to better predict the directive of individual stock returns for listed German fi rms. The results of this study, though different from most other studies of other stock markets, indicate the Frankfurt stock market behaves in similar ways to North American, other European and Asian markets previously studied in the same manner.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -This paper seeks to study capital market efficiency, because results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Hong Kong, the third largest exchange in the Pacific-Basin of Asia. Design/methodology/approach -The weak form of the efficient markets hypothesis is examined to indicate its usefulness in terms of the results of this study. Do the data indicate that the times series of closing prices is a random walk or are their predictable properties? Findings -It will be noted from the results that the model identifies predictive short-term properties that exist in the data of returns of Hong Kong Exchanges for the period studied. Research/limitations/implications -Conclusions are limited to those firms studied and the time period covered. Originality/value -For the securities exchanges in Hong Kong, evidence indicates that the weak form of the efficient markets hypothesis does not characterize the trading market.