In today's Global Economy copious amounts of capital are traded through the Stock Markets; these economies are strongly linked and heavily influenced of the performance of their Stock Markets, further, the Markets have become a more accessible investment tool, not only for strategic investors but for common people as well, therefore they are not only related to macroeconomic parameters, but they influence everyday life in a more direct way. The characteristic that all Stock Markets have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the Stock Market is selected as the investment tool, uncertainty is part and parcel of the Stock Market. The best that one can do is to try to reduce this uncertainty. Stock Market Prediction (or Forecasting) is one of the instruments in this process.

Econometrics is derived from several disciplines, including mathematical economics, statistics, economic statistics, and economic theory. The goal of econometrics is twofold: to give economic theory empirical data and to empirically verify it. It is a study that produces measurements, where qualitative data is turned into quantitative mathematical forms. Once this is performed, these statements can then be empirically proven, disproven, measured, and compared.

The recent widespread availability of intraday tick-by-tick databases for stocks, options and currencies has had an important impact on research in applied financial econometrics and market microstructure. Recent quantitative modeling tools such as intraday duration models and GARCH models are useful.

Traditional Time Series Prediction: The Traditional Time Series Prediction analyzes historic data and attempts to approximate future values of a time series as a linear combination of these historic data. In econometrics there are two basic types of time series forecasting: univariate (simple regression) and multivariate (multivariate regression). These types of regression models are the most common tools used in econometrics to predict time series. The way they are applied in practice is that firstly a set of factors that influence (or more specific is assumed that influence) the series under prediction is formed. These factors are the explanatory variables xi of the prediction model.

Then a mapping between their values xit and the values of the time series yt (y is the to-be explained variable) is done, so that pairs {xit , yt} are formed. These pairs are used to define the importance of each explanatory variable in the formulation of the to-be explained variable. In other words the linear combination of xi that approximates in an optimum way y is defined. Univariate models are based on one explanatory variable (I=1) while multivariate models use more than one variable (I>1). Regression models have been used to predict stock market time series. A good example of the use of multivariate regression is the work of Pesaran and Timmermann (1994) .They attempted prediction of the excess returns time series of S&P 500 and the Dow Jones on monthly, quarterly and annually basis. The data they used was from Jan 1954 until Dec 1990. Initially they used the subset from Jan 1954 until Dec 1959 to adjust the coefficients of the explanatory variables of their models, and then applied the models to predict the returns for the next year, quarter and month respectively.

An example of time series forecasting in econometrics is predicting the opening price of a stock based on its past performance.

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