In this study, we investigated of contagion unanticipated shocks of oil price, exchange rate and gold price on the stock market in Iran using DFGM model, aiming to explain the dependence of financial markets in times of fluctuation financial markets in times of volatility. The data includes weekly Tehran Price Index, gold price, oil price and exchange rate from November 2008 to August 2019. The results show that about 99% of the exchange rate fluctuations and the total stock price index in the pre-crisis period are explained by the specific factor. But after the currency shock in January 2018, it was observed that at the end of the year 2018, 88 and 63 percent of the exchange rate fluctuations and the total price index of Tehran Stock, were explained by contagion factor, respectively. The results also show that the currency crisis (currency fluctuations), it has contagioned to the stock market. In addition, evidence of the impact of oil and gold price shocks on the Tehran Stock Total Price Index and the exchange rate was obtained.

US 2008 financial crisis، spreaded out across the international stock markets in the second half of the year 2008. The crisis decreased the returns and increased the volatilities of almost all of the stock indices، however، its effects on Iran’s stock markets were unexplored. Financial limitations create an exclusive situation in Iran’s stock market. Using weekly data on the stock indices from July 2006 through August 2009، this study aims to exam in the contagion effects of the global financial crisis through the main indices such as S&P500 and FTSE100 on Tehran Stock Exchange indices: industrial and financial intermediary indices and The First and the Second indices of Tehran’s Stock Exchange. The DFGM2 contagion test model is employed to identify the contagion effects. Our findings reveal that the crisis has been transmitted to Tehran Stock Exchange index and has decreased the index. Moreover، the industrial and the First indices are affected by the crisis negatively، however، the results shows that the crisis did not have any significant effects on TSE’s financial intermediary and the Second indices.

The purpose of this article is modeling the behavior of stock price using stochastic differential equations. The data for this study include daily observations of the total stock market index, the index of the top 50 companies and the index of the 30 largest companies in the Tehran Stock Exchange. The data are daily from March 25, 2006 to April 15, 2015.The geometric Brownian motion and geometric Brownian motion with nonlinear GARCH are used to modeling the behavior of price index. The results of this study includes the following: (1) According to the log likelihood function, geometric Brownian motion with nonlinear GARCH in the three groups studied data has better performance than the geometric Brownian motion. (2) Based on the model of stochastic differential equations with stochastic volatility, the total market index is more influenced by the good news. (3) The impact of the bad news on the index of the 30 largest companies is more than the impact of the good news. (4) The unconditional variance of the total stock market index has two structural breaks; the unconditional variance of index of the top 50 companies has one structural break and no structural breaks in the unconditional variance of index of the 30 largest companies.

The main goal of liberalizing markets, which has been followed by most developing states in the past decades, is to potentiate domestic saving, attract foreign capital, and improve efficiency of financial resources. This paper studies impact of financial liberalization on the stock market in the Iranian economy. To do this, seasonal data for the period of 1994-2008 have been studied using vector self-regression method as well as co-integration tests. The findings show that in the long term, liberalizing financial markets will have a positive impact on total price index of the stock market due to development of financial system, though it has no meaningful effect on total price index of the stock market in the short run.

Modeling price fluctuations in financial markets is very important. We try to model price fluctuations in Tehran stock exchange using heterogeneous agents’ model. We used agent-based computational approach. In this model, there are two kinds of agents, some agents have extrapolating expectations (chartists) and others have stabilizing or mean-reverting expectations (fundamentalists). The dynamics of shares of these two types of agents make price fluctuations. For determining the relative effectiveness of agents expectations, Diechi and Westerhoff (2012) method, is used. For this purpose, weekly data of Tehran Stock Exchange price index (TEPIX) from 1997 to 2014 were used. Modeling results show that the relative sensitivity of buyers with different expectations, and their relative impact to aggregate demand, have significant and important role in the price dynamics of Tehran stock market. We also show that the relative impact of chartists to price fluctuations very important and over the past two decades, the share of them from aggregate demand have been more than 80 percent.

As far as economic-financial issues are concerned, inflation is a phenomenon that has attracted the attention of many economists and scholars in recent decades. This article examines the relationship between the whole – sale price indexes of merchandise and the formal stock rates of firms, i.e. whether there exists a meaningful dependency between inflation and stock price and whether the stock can effectively function as an anti-inflation factor. Also, the impact of inflation on the stock price of 18 industrial centers was studied to determine the stock of which industry is in more coordination with the emerging inflation.To identify the type of dependency between the whole-sale price and the stock price, regression analysis was used. The whole-sale indexes were considered as the independent variable (X) and the stock price indexes of the 18 industries (y1,y2,...,y18) as the dependent variable. To render the regression analyses meaningful, a T-test measure was justifiably applied and to show the scale of correlations among the variables, a correlation coefficient (R) was worked out. Having the regression and other statistical indices calculated, the equations attained were detected via Durbin – Watson’s Test (D-W).The statistical data regarding the total stock price index, the stock price index of the 18 industries and the whole-sale price index are concerned with the period, 1369 until the end of Shahrivar, 1377. The research findings show that there exists a direct and positive correlation between the whole-sale price index of merchandise and the total stock price, but this correlation is not radically biased to any of the components highlighted above.On the other hand, the results indicate that inflation exercises no considerable effect on the stock price of the validated companies. It is worth mentioning that the correlation between the whole-sale indices of merchandise and the stock price indices of most industrial centers is direct and positive, but its intensity is quite different depending on the type of the industry. This, in fact, suggests that there is a strong relationship between inflation and the stock price of the industries in question.

The oil price rise and fall forms one of the serious factors that really affect consumers, producers and Markets especially in terms of costs, trading strategies and incentives to launch new investment in technology or reorganize former ones. In the literature, there are many studies on the effects of oil price shocks on economic activity. Recently, the relationship between oil price and stock returns has come to the forefront of public attention and this probably because of the fact that Crude oil prices have been showing an exceptional volatility which has led to an increase in uncertainty of the energy sector, the whole economy as well as the financial markets. Despite the significant body of research has examined the effect of oil price shocks on stock market in oil importing countries, only a few researchers focused their attention on exploring how changes in oil prices influence the stock market in oil producing countries. The effect of oil price shocks on the stock market is a meaningful and useful measure of their economic impact. Since asset prices are the present discounted value of the future net earnings of firms, both the current and the expected future impacts of an oil price shock should be absorbed fairly quickly into stock prices and returns, without having to wait for those impacts to actually occur. In other side, large literature has reported that oil price fluctuations have an asymmetric impact on the macroeconomy. That is, while oil price spikes lead to reduced outputs, oil price drops do not necessarily lead to an increase in output. Iran’s economy is highly dependent to oil revenues such as other oil exporting countries, so that Iran’s economy and also stock market can affected by oil price shocks. With regarding importance of oil price changes on Iran economy, the aim of this study is to investigate the asymmetric impacts of oil price shocks on Tehran Exchange Price Index (TEPIX).In this study, the relationship between oil price shocks and TEPIX from 2000:6 to 2010:11 have been investigated. For this aim, the mothod of vector autoregressive regression (VAR), impulse response function and variance decomposition with three control variables of liquidity, constraction price index and gold price have been used. For investigating the asymmetric effect of oil price shock, we use Mork (1989) and Hamilton (1996) approach. Mork (1989) proposes an asymmetric definition of oil prices, which distinguishes between positive and negative changes, which have been defined as follows: Real oil price increase: doilt(+) = max [0, doilt] Real oil price decrease: doilt(-) = min [0, doilt] Hamilton (1996) proposed the concept of net oil price increase/decrease. Net oil price increase (NOPI), which is the percentage change of the increase of oil price if the price of the current month (t) exceeds the twelve previous months’ maximum. If the price of month (t) is lower than it had been at some point during the previous twelve months, the series is defined to be zero for period (t). So: NOPIt = max [0, oilt – max (oilt-1, oilt-2, oilt-3……oilt-12]Similarly, net oil price decrease (NOPD) can be defined as:NOPDt = min [0, oilt – min (oilt-1, oilt-2, oilt-3……oilt-12)]The investigation of the asymmetric effects of oil price shocks on TEPIX by Mork (1989) and Hamilton’s approach revealed that oil price shocks have asymmetric impacts on TEPIX and in both approaches, oil price decrease has greater share in explanation of forcasting error variance of TEPIX respect to oil price increase. The result of Impulse response function showed that, effect of one standard deviation shock in oil price increase variables, is positivein primary period and ofter that convergence to zero. The results also show that one standard deviation shock in oil price decrease has positive effect on TEPIX. Forcasting error variance de-composition of TEPIX showed that in all period, oil price increase after the TEPIX has the largest share in explanation of TEPIX’s forcasting error variance that it is increasing by the time. One standard deviation shock in oil price changes has positive impact on TEPIX that it is not constant during the time. The explanation of the positive connections between oil price shocks and the stock returns and the positive effects on the volatility is intuitive. Since oil export is a substantial source of GDP in Iran, the increase in oil price leads to economic growth by creating significant higher oil revenue. Consequently a rise in oil prices effect on expectation and this affects seriously the stock price.