One of the most important market in each economy in financial markets that is strongly affected by other economic variables such as Oil Price shocks. One of the most important problems in Oil export in country is the volatility and Instability of Oil Price. For this reason in this Thesis we are intendent to evaluate effect of Oil Price shocks on the stock market in iran. Fot this reasen we have used One of the most famous time series model, that is SVAR to analysis this shoks. The result of this research shows that the relative importants of Oil Price shoks in comparation to other variable of the model is not so high.

The movements in Oil Prices are complex and, therefore, seem to be unpredictable. The traditional linear structural models have not been promising when applied to forecasting, particularly in the case of complex series such as Oil Prices. Although linear and nonlinear time series models have done much better job in forecasting Oil Prices, there is yet room for an improvement. If the data generating process is nonlinear, applying linear models could result in misleading forecasts. Model specification in nonlinear modeling can also be very case dependent and time-consuming. In this paper, we model and forecast daily futures Oil Price, listed in NYMEX, applying ARIMA, and GARCH models, for the period April June 1983 - Jan. 2003. Then, we test for chaos using BDS, Lyapunov exponent, Neural Networks, and Embedding Dimension methods. Finally, we will set up a nonlinear and flexible ANN model to forecast the series. Since the tests for chaos indicate that the Oil Price in futures markets is chaotic, the ANN model should make better forecasts. The forecasts comparison among the models approves that.

the relationship between the Price of Oil and the level of economic activity is a fundamental empirical issue in macroeconomics. In this research, by using a multivariate GARCH-in-Mean VAR, we try to investigate direct effects of uncertainty of Oil Price on macroeconomics of Iran by using annually data from 1965 to 2013. Results show that uncertainty about Oil Prices had a negative and significant effect on real output in our sample.

In this paper, hybrid intelligent framework presented as a new and efficient method for crude Oil Price forecasting. This method is developed by applying a systematic integration of GMDH neural networks with GA and Rule-based Expert System (RES) with Web- based Text Mining (WTM) for crude Oil Price forecasting. In WTM, information about agents that affect on crude Oil Price outperforms from different sites and degree of these effective agents restores as rules by RES. These rules with long run and short run crude Oil Price moving averages are modeled by GMDH neural network and results reveal that hybrid intelligent system improve forecasting results.