Modeling Financial Time Series Data.

      Аннотация: In recent years, stock markets have become an important part of many countries' economies. This increasing importance of stock markets has motivated me. Economists to predict stock prices and financial returns. In addition, estimating stock market fluctuations is an important practice among investors and policymakers. Suitable statistical tools are very important to study the intercaches exits in the stock prices time series data. Accordingly, various econometric models have been employed in this investigation to study the different stock market behaviors and the dynamic relationship in the data sets.

Ключевые слова: Augmented Dickey-Fuller, Akaike Information Criteria, Autoregressive Integrated Moving Averages, error correction model, Canonical Correlation Model, Canonical loadings, Canonical weight, Error Correction Mode