الفهرس 
المحتوياتيوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
Prediction of macroeconomic financial variable is one of the primary functions of macroeconomic time series models, including dynamic factor model, dynamic stochastic general equilibrium model and vector autoregressions model. This study establishes methods that improve the predictions of these models, by combined models dynamic stochastic general equilibrium, dynamic factor and vector auto regressions. This study aims to identify the best forecasting model of stock prices and exchange rates to be more accurate regarding the problem of financial time series fluctuations. In this study we are made comparison between DSGE model, dynamic factor model, vector auto-regression model, DSGE with vector auto-Regression model, DSGE with dynamic factor model, dynamic factor with vector auto-regression model and combining DSGE model and dynamic factor model with vector auto-regression model in forecasting stock prices and exchange rates.This study reaches that using combining DSGE model and dynamic factor model with vector auto-regression model is the best and the most accurate one in forecasting stock prices and exchange rates. The applied study with agroup of daily data of stock prices of commercial international bank (CIB), financial group hermes and group of daily data of exchange rates of american dollar and Euro. The study recommends extending the use of time series analysis as an effective tool in studying many financial variables and forecasting of it. Also, the study emphasizes the importance of volatility in this kind of data not only as avariable has great importance but also as a necessary explantory variable in understanding the behavior of many variables in finance.