Applied researches in many financial markets show that Stock price index will fluctuate with changes in macroeconomic variables. Since the stock value in theory is equal to the sum of discounted future cash flows and these cash flows are affected by macroeconomic events. Therefore they can be also affected by Oil shock. This study aims to investigate asymmetric effects of Oil price shocks on the stock price index in Iran. In this regard, SVEC model with Bootstrap Confidence Interval in Impulse Response Functions are used.The estimation results of Impulse Response Functions and Forecast Error Variance Decomposition indicate that positive Oil price shock has an increasing effect on stock price index while negative Oil price shock decreases the stock price index. Also the results show that the effects are asymmetric, So that the effect of negative Oil price shock on the stock price index is far larger and more lasting than the positive Oil price shock. The results of confidence intervals in IRFs indicate that impulse responses of stock price to Oil shocks are reasonable and significant in all cases.

According to standard new Keynesian theory; an Oil price shock pushes costs and increases inflation while lowers output. This relationship is modeled by an improved new Keynesian Philips curve for Oil importing countries. However, Oil price shock in the Oil exporting countries has an additional channel of transmission through Oil revenues called wealth effect. This channel do not exists in the standard new Keynesian Philips curve. In this paper, we add Oil sector to the standard two-sector new-Keynesian model in order to analyze the cost push and wealth effect of Oil price shock in Iranian economy. The developed new Keynesian Philips curve for Oil exporting countries in this model depends on real wage, Oil price, productivity of both sectors and Oil revenues. The dynamic stochastic general equilibrium model is calibrated based on Iranian economy parameters and then solved for impulse response functions. Our results show that wealth effect of Oil price shock on inflation is higher due to lower Oil share in short term production function and controlled Oil prices. The increasing impact of wealth effect on consumption in this model is more than the decreasing effect of cost push. Although labor supply lowers slightly after Oil price shock the overall effect on employment and real wages are negative. Consequently, it can be concluded that Iranian economy enters a stagflation after an Oil price shock with consumption growth and lower real wages.

The current paper aims to explain the role of banks' market power in creating liquidity during an Oil price shock. The sample population of this study includes Iran's commercial and specialized banks whose information is available from 2011 to 2021. To test the research hypotheses based on the multiple regression method, the data were analyzed using Eviews 10 software after checking the data's validity and the regression analysis assumptions. The Lerner index was used to measure the bank market power variable as the research's independent variable. The inverted index of stable funding net ratio based on Basel 3 liquidity requirements was used to measure the study's dependent variable, liquidity creation. Furthermore, the negative changes in Oil prices were considered Oil price shocks, which is the moderating variable of the present study. The results showed that bank market power has a positive and significant effect on the creation of bank liquidity at the confidence level of 90%, which means that with a high market power, banks tolerate more liquidity risk. Besides, the results indicate that the Oil price shock has a negative and significant effect on bank liquidity creation at the level of 90\%; Oil price shock does not influence the relationship between bank market power and liquidity creation. Therefore, banks with high market power tolerate more liquidity risk, and market power can significantly impact economic growth through its effect on creating liquidity.

According to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors and activists in the stock market. In this study, based on data related to 140 companies listed on the Tehran Stock Exchange. we have attempted to predict stock price shocks with emphasis on financial ratios. In order to select the optimal variables from the set of 96 variables, two evolutionary algorithms of particle swarm optimization and genetic algorithm have been used. After applying the mentioned algorithms, finally, 8 variables affecting permanent and temporary shocks were extracted, which in the regression model mentioned in the research, their effect on the predictor of shock was investigated. the results of RSME model are the permanent shock (genetic algorithm), permanent shock (particle swarm optimization), temporary shock (genetic algorithm) and temporary shock (particle swarm optimization (particle swarm optimization), 5. 8433, 5. 6284, 7. 537 and 7. 295. as we observe, RSME in permanent shock based on genetic algorithm is more than RSME permanent shock model based on the evolutionary algorithm of particle swarm optimization. also in the transient shock model based on the genetic algorithm, the model is more than RSME of the temporary shock model based on the evolutionary algorithm of particle swarm optimization. It can therefore be stated that the estimated regression is based on the selected variables from the evolutionary algorithm of the particle swarm optimization, and has better predictive power than the selected variables of the genetic algorithm.

Residential investment is the main component of investment and has a crucial rule in output and employment. The residential investment is affected by macroeconomic shocks. This paper provides an empirical assessment of the pace at which housing investment has responded to different macroeconomics shocks in Ian in the 1978-2017 period by using the BVAR method. Four macroeconomic shocks are introduced to the model i. e. exchange rate, monetary, fiscal, and Oil income shocks. The Sims-Zha (Normal-Wishart) was recolonized the most appropriate prior function for our data set. The Impulse response functions reveal that one standard deviation positive shocks to exchange rate, interest rate, government expenditure, and Oil income initially increased housing investment and then gradually returned to its steady-state value. Oil price and monetary shocks had the shortest and longest duration impact on residential investment. The highest volatility in residential investment created by the fiscal, monetary, exchange rate, and Oil income shocks respectively. The results indicate that the policymaker should consider the difference in the amount and duration of the impacts of four macroeconomic shocks on residential investment in the policymaking.