جستجوی مقالات مرتبط با کلیدواژه « بازار ارز » در نشریات گروه « علوم انسانی »

The efficiency of the foreign exchange market is crucial because exchange rates play a vital role in balancing a nation's internal and external economies. Iran's foreign exchange market has undergone significant developments and fluctuations in recent years, impacting its efficiency. This is evident in the monthly time series data for the unofficial exchange rates of the dollar, euro, and yuan from 2001 to 2021. An interesting trend has emerged since 2017: the Chinese yuan appears to be gradually replacing the dollar and euro as the primary trading currency in Iran's international market. Statistical tests, including the Augmented Dickey-Fuller (ADF), Phillips-Peron (PP), and KPSS tests, indicate weak-form efficiency in the markets for all three currencies (dollar, euro, and yuan). This suggests that these markets remain vulnerable to speculative attacks. Furthermore, Johansen's cointegration and Granger's causality tests reveal an absence of semi-strong efficiency in all three markets. This implies that past price information alone is insufficient to predict future exchange rates. The research findings suggest that choosing the yuan as an alternative currency has had a somewhat positive impact on improving the efficiency of the Iranian currency market.

One of the most important issues in financial knowledge which is related to portfolio selection, efficiency market and asset allocation is spillover effect between markets and this effect includes return, volatility and shock effect. These days every shock or volatility in one market effect on other markets. Correct identification of spillover effect is very important. This paper aims to measure and analysis spillover effect between stock, currency, gold-coin, oil and housing markets. For these purposes we collect daily data of stock, currency, gold-coin, oil and housing for the time period of 2009 to 2020.we used VARMA-BEKK-AGARCH model for estimation. Results show return spillover from currency to stock and from stock to housing and shock spillover from currency, gold-coin and oil to stock and also volatility spillover from currency and gold-coin to stock and from stock to housing. Besides result show leverage effect of shocks from stock to housing market.so we suggest in order to minimize investment risk we had better to evaluate the spillover effects in selecting markets(assets)for our portfolio.

In this paper, the asymmetric effects of stock market shocks on the exchange rate in Iran were evaluated using the daily data from 20/03/2016 to 21/06/2023 using cointegration and APARCH methods. The evidence shows that both shocks (positive/negative) are repeatedly found in the Iranian stock market. Out of 2642 days, 1601 days of the market had negative returns and 1041 days had positive returns. In 511 days the market has fluctuated more than one standard deviation (big shocks). Estimates show that there is a strong long-term relationship between these two markets. The findings showed that the reaction of exchange rate to the positive and negative shocks of the stock market is asymmetric and has a long-term effect on the exchange rate. The behavior of the exchange rate towards negative and positive impulses is cyclical. The big negative shocks in the stock market gradually increase the return of exchange rate and eventually this effect does not disappear and puts the exchange rate at a higher level. But the effect of the big positive shocks in the stock market on the exchange rate is dampening. Negative shocks have a greater impact on conditional volatility compared to positive momentum. Also, the fluctuation of the stock market shows a very stable pattern. 

Identification and quantification of spillover effect in financial markets is one of the most important topics of financial knowledge. recognizing the spillover channels and measuring spillover effect in financial market will help us to prevent disorder and disruption in markets and by settling stable situation in markets will provide economic growth and welfare. this paper investigates and measure spillover effect at different economic sanction periods. Due to the history of economic sanction imposed by western country at some periods such sanctions were raised and at some periods such sanction declined so for more precise appraisal of sanction role in return spillover, we divided research periods into four sub periods included two periods of severe sanction and two periods of no severe sanction. In this order we collect daily data of sock, currency and gold coin markets for the periods of 2008 to 2022 by applying the VARMA-AGARCH model for measuring and analyzing. Results presents unidirectional volatility spillover effect from currency and gold coin market into stock market and bidirectional volatility spillover effect between currency market and gold coin market. by dividing research periods into four time periods, volatility spillover effect has been seen for second and fourth time periods which are proxy for severe sanction periods but volatility spillover effect has not been seen for first and third time periods which are proxy for no severe sanction periods. results showed direct and positive relation between intensity of economic sanction and volatility spillover effect in parallel markets.

A futures contract is an agreement to simultaneously buy and sell a certain amount of one currency against another currency at a certain time and at a certain rate in the future. In fact, the futures contract is a type of forward contract, with the difference that the buyer and the seller are not known to each other and only transact with each other through banks or brokers active in the currency market. The foreign exchange market is a virtual market where interested people around the world can buy and sell existing currencies through the Internet and selecting the desired broker. The distinguishing feature of this market compared to other markets is the limited selection of investment options, and the major forex transactions are carried out only on seven currencies and three currency pairs, and by examining the macroeconomic variables of the countries and the news available at the international level, you can choose your desired currency. Islamic jurists believe that it is possible to trade the currencies of different countries in cash or at different rates because the inherent price and purchasing power of the currencies of different countries are independent and the general agreement is about the inaccuracy of using futures contracts and currency advances in the state of delaying obligations and rights. There are parties to some time in the future, which in this article has been examined in jurisprudential and legal doubts related to the authenticity of these contracts.

The transmission of financial crises between different markets in an economy indicates the existence of channels of transmission of this crisis. Today, parallel currency markets are closely related to other markets such as gold, coins, stocks and oil. Channels that transmit shocks and fluctuations of the foreign exchange market to other markets can include information, macroeconomic variables, investment behaviors, and so on. In this study, using daily data from 2009 to 2017, the explanation of overflow fluctuations and shocks in the foreign exchange market and how to transfer these shocks to other markets has been examined. The results indicate the existence of fluctuations overflow as well as structural fractures due to the presence of this overflow. The research model and determination of interruptions is based on the VAR model. Yields and fluctuations as well as the presence of the Arch effect in the model are determined based on the VAR model. The MV-GARCH model is used to determine the returns in the foreign exchange market. Fluctuations and shocks of the foreign exchange market and its impact on other markets as well as future prices in different markets are determined based on the VAR model. The results of this study indicate the effect of shock in the foreign exchange market on the trend of future prices in this market as well as the impact on other markets.

In addition to the direct effects, Economic sanctions indirectly distort the economy by influencing the psychological layers of the society, and its impact is no less than the direct effects. Among the psychological and expected effects of sanctions is the impact on economic markets; So that stimulating the expectations of market players based on an external factor such as sanctions can lead to increased turbulence in the markets and increases economic risk. This article is based on examining the psychological effects of economic sanctions on the foreign exchange market as a central market in Iran's economy and examines the issue of the conditioning of this market in relation to the sanctions. In order to investigate the effect of the psychological aspects of sanctions at different decile levels, the method used in this research is quantile regression, which analyzes the issue using seasonal data of Iran's economy from 2011 to 2021. The obtained results show that the effect of sanctions on exchange rate fluctuations is significant and positive; this means that with the increase in the psychological burden of sanctions, the fluctuations of exchange rate have always increased significantly, and this confirms the Conditionalization of the economy towards foreign issues. This issue has caused the issues related to sanctions toleave their effects on the exchange market, even if they do not actually have any effect, and Iran's economy has faced a higher risk. Finding ways to minimize the psychological aspects of sanctions is one ofthe main suggestions of the current research.

The experience of large fluctuations in the Foreign Exchange Market in Iran and the impact of monetary and fiscal policies on this market revealed the importance of adopting a scientific approach by fiscal and monetary policymakers and paying attention to the behavior of other actors (Foreign Exchange Speculators) in the decision-making. The present study tries to evaluate the Social Loss of adopting different approaches by monetary and fiscal policymakers for 9 Years (2011-2019) using the Game Theory approach via identifying the optimal interactive approach between the Government and the Central Bank that leads to the least amount of Social Loss. According to the results, the least Social Loss is achieved when the Monetary Actor pays more attention to the stabilization of Interest Rates and has more Independence in decision-making. In other words, the increase in Interest Rates by the monetary authority increases the desire for Foreign Exchange demand by Foreign Exchange speculators, as well as the desire to further divert government spending from the balanced government budget, which both behaviors interpreted to increase Social Losses. Accordingly, by resorting to the criterion of Social Loss, we can recommend the instrumental independence of the Central Bank to increase Social Welfare. This result requires a change in the conduct of Monetary and Fiscal Policies and a change in the attitude of the country's economic officials about the independence or non-independence of the Central Bank and how it interacts with the Government.

Exchange rate is a measure of the equality of a country's currency against the currencies of other countries. It indicates the measurement of that country's economic situation in comparison with other countries. In the framework of conventional economic theory, the exchange rate system refers to the mechanism of determining the exchange rate through market forces exerted on the supply and demand.The purpose of this study is to understand the dynamics governing the exchange rate behavior using nonlinear models. By understanding the exchange rate dynamics, one can recognize its unusual and worrying behavior over time and apply the necessary policies accordingly.

The baseline linear model used in this study is a finite-order autoregressive (AR) model with relation (1):(1) In the real logarithm of the real exchange rate, the interrupted polynomials are placed in the roots of ϕ (z) = 0 on or outside a single circle. The roots outside the unit circle mean that PPP remains stable in the long run.Regime change regressionTwo-mode TAR (2) models are defined as follows:(2)  In the above relation, there is a sequence of white noises with mean zero and variance 1. In this model, it is assumed that the variance in each regime is different from that in the other regimes. In order to complete the above definition, the R1 and R2 regimes need to be described more precisely. It depends on how each regime changes over time.Introducing the Bayesian modelIn order to estimate the Bayesian SETAR, we assume that the exchange rate variable has a normal distribution.(3)  In the SETAR model, the regime change is defined as a discrete variable as follows:In the next step, in order to have a Bayesian estimate, we need to specify the backgrounds of the model coefficients and the other parameters. An appropriate assumption in this regard is to assume that the anterior distribution r is a continuous uniform distribution whose boundaries include the minimum and maximum time series data as follows:In the next step, we will define the backgrounds for the coefficients. According to the objectives of this study, we will use the background for the ignorance of the normal part as follows:Bayesian estimation methodThe basis of Bayesian inferences is Bayesian theorem. According to this theorem, the posterior probability of an event varies according to the product of the previous probability in the logarithm of the orthogonality. In mathematical terms, Bayes' theorem is as follows:

Table 1 reports the results of the SETAR model estimation for the exchange rate return (Rials against the dollar with the monthly rotation in the period from 2004 to December 2020). The validity intervals of the coefficients are adjusted and do not include zero, which, like the classical case, is based on the significance of these  coefficients. Table 1. Bayesian SETAR model coefficients (1) for the dollar exchange rateCoefficients Posterior average Posterior  standard deviation95% confidence interval 0,00790,0023(0,0034;0,0123) 0,31790,0713 (0,1773;0,4567) 0,01470,0161(-0,0172;0,0465)  0,43380,1468(0,1427;0,7244) 0,02230.0006 (0,0208;0,0230) Table 2. Variances of the two regimes in the Bayesian  SETAR (1) model for the dollar exchange rate Coefficients Posterior  average Posterior standard deviation95% confidence interval 0/00070/0001(0,0006;0,0009) 0/00940/0019 (0,0064;0,0137)  According to the results of Table (2), when the exchange  rate is lower than the latter value of thresholds, its variability is much less than when the  exchange rate is higher than the threshold value (variance in regime 2 is greater than that in regime 1)Figure (1) shows the autocorrelation of the simulated values in the latter estimation of the model parameters in the two regimes. Figure 1. Autocorrelation of the posterior coefficients in both regimes 1 and The results in Figure 1 show that the correlation of the simulated values for all the model parameters rapidly decreases to zero. Therefore, we are faced with a suitable sample of values to simulate the posterior distribution of the parameters. There is also no need to increase the simulation volume.Figure (2) shows the effect curves of all the later parameters of the model used in this research: Figure 2. Effect diagrams for the SETAR pattern parameters (1) Based on the findings about the curve of the effect related to all the parameters, no regular pattern exists in the simulated values ​​of the parameters. Therefore, the stability of these coefficients is confirmed, and the results of the Bayesian model SETAR (1) used in this study are statistically valid. 

The exchange rate as a price variable plays a very important role in the performance of an economy. The results of this study indicate that there are two exchange regimes in which the exchange rate adjustment parameter will be in equilibrium with a 95% probability in regime 1 (0.1773,0.4567). This is very small due to the deviation of the latter standard of this coefficient (S. Dev = 0.0713). Also, the same parameter with a 95% probability in regime 2 will be at the distance (0.1427, 0.7244), which is a relatively long distance. The results showed that this is due to the high volatility of the exchange rate in regime 2. In addition, the adjustment in the first regime to the long-term equilibrium path is much safer than the adjustment to the long-term path in the second regime because the variability in the conditions of the exchange rate increase is very high. Finally, the results of this study showed that the expansionary exchange rate regime (regime 1) has a deviation from higher regime standards than the mild exchange rate regime (regime 2), which indicates high currency fluctuations in this regime and more uncertainty. So, using regime 1 provides the conditions for a proper economic growth in the future.

One of the effective factors in managing and controlling sanctions is managing its psychological effects. Naturally, sanctions have a number of psychological effects, including fluctuations in the vital markets of the economy, and as a result, dealing with them properly plays an important role in managing market fluctuations. This article analyzes the interaction between the psychological aspects of sanctions and foreign exchange market, housing and stock market risk using the (VAR) model. The results of this model show that there is a correlation between the psychological effect of sanctions and the risk of market in the short and longterm. The psychological effect of sanctions increases the risk of foreign exchange and stock market and reduces the risk of fluctuations in the housing market, and in contrast, the increased risk of currency and stock market fluctuations leads to an increase in the psychological effect of sanctions on society. In other words, the public perception and mental image created in society show the effectiveness of sanctions and this is a great help to sanctioners to increase pressure. it is suggested that by minimizing the psychological aspects of sanctions, showing their insignificance, the pressures of market fluctuations are largely controlled.

After making economic theories, the main goal of researchers in economics is to measure economic relations more accurately. For this purpose, various methods are used in economics to provide a better insight into the functions of economics. The evolution of knowledge in different approaches and methods of measuring economic relations is, thus, happening very fast.After the US withdrawal from the Barjam nuclear deal between Iran and the P5 + 1 countries in early 2018, relatively long turbulent waves occurred in the Iranian foreign exchange market. The overflow of these turbulent waves, in a short time, disturbed some markets, including financial markets, gold, currency and housing. The stock market, as a major financial market in Iran, did not show strong evidence of currency turbulence overflow at the beginning of these developments. Gradually, over time, the growth of the average stock market index along with the stagnation of transactions in a market such as the housing market showed the possibility of a long-term relationship between foreign exchange market movements and the stock market. The existence of empirical and accurate knowledge of such relationships leads to improved turbulence control by stabilizing the country's financial markets. On the other hand, the development of Iran's economy depends on improving the efficiency of financial markets, which necessitates such knowledge. The short-term relationship between the foreign exchange market and the stock market does not pose a problem in terms of financial theories. However, the long-run relationship, referred to in the economic literature as co-integration, is at odds with Market Efficient Hypothesis. This hypothesis states that dealers in the so-called markets behave rational and use all available information to discover the future trend of stock prices. Hence, stock price movement is random, and the long-term relationship between the foreign exchange market and the stock market violates the Efficient Market Hypothesis; such a relationship can be used for future stock market trends.

In financial economics, the co-integration of non-stationery variables is very important. This is because, despite the unpredictability of certain time series, their linear composition is predictable and can be deduced through standard methods. The empirical results suggest that the relationship between the exchange market and the stock market is inconsistent. Various factors lead to such a contradiction addressed in the present study. Here, using some empirical facts about unconditional distribution of financial data, a new Bayesian Method which involves the Partial Bayesian Residual-based Test is introduced and applied. This approach was proposed as an alternative to classical testing methods so as to estimate long-term parameters. There are also alternative methods to the OLS method, which provides only one co-integration relationship. These alternatives offer a consistent and efficient estimate of the long-run relationship. In this case, we can refer to the Dynamic Ordinary Least Squares method (DOLS) and the Fully Modified Ordinary Least Squares method (FMOLS), which were proposed by Stock and Watson (1993) and Phillips and Hansen (1990), respectively. In the present investigation, the FMOLS method has been used to make an efficient estimate of the regression coefficients of the long-run relationship, Inder (1993) used Monte Carlo simulations to show that the estimation of the long-run relationship using the FMOLS method is more appropriate than the OLS method, especially in large samples. This is because the bias of the parameter estimation reduces in long-run relationship significantly. It leads to the creation of residuals that more accurately reflect the structure of their generating process, which is very effective in the performance of the Partial Bayesian Test used in this study; the financial data are not normally distributed, contrary to the classical approach of co-integration tests. This study uses a Residual-based Co-integration Test that explains the behavior of financial data more accurately than a normal distribution approach. It is worth mentioning that this test considers the mentioned test as a special case of normal distribution. In this respect, it has a more general preference for modeling in our investigation.

The test was conducted using simulated data in different simulation quantities for two processes. The results confirmed the existence of co-integration between these two processes. It is worth noting that, to estimate the posterior distribution of the parameters of a Bayesian model, it is necessary to calculate the Marginal Likelihood Function of the parameters obtained through integration. However, when the Bayesian model has no mixed-scale normal distributions based on that inference Bayesian model, the integral cannot solve the problem by using analytical methods. In this case, a method such as the MCMC (Markov Chain Monte Carlo) Simulation must be used. Since the correlation hypothesis test in this study was not a co-integrated vector, the MCMC method was used to estimate the real exchange rate and the stock price data. The test results obtained with the Partial Bayesian method show a positive long-run relationship between exchange rates and stock prices. The indication of a co-integration between the stock market and the foreign exchange market means that the future trends of the stock market in combination with the foreign exchange market are predictable.

Based on the results, the long-run relationship between the exchange rate and the stock price index is positive. It is indicated that a one-unit increase in the exchange rate will lead to a 2.5-unit increase in the stock price index. This means that a linear combination of stock prices with the exchange rate is predictable and, thus, contradictive of the Efficient Markets Hypothesis about the stock market in Iran. In other words, the Efficient Markets Hypothesis about the Iranian stock market is rejected.

Due to the US breach of JCPOA and leaving this international agreement, and imposing new sanctions against Iran, we are witnessing several problems in the economy, and especially difficulties regarding to foreign currencies. Extreme fluctuations and sharp increases in the foreign exchange rate have led to the increased level of the prices and decrement of the purchasing power of the people. Currently what is perceived as the most important is the implementation of Eghtesad-e-Moghavemati. Therefore, in this research, we have classified the related indicators of Eghtesad-e-Moghavemati to foreign exchange market which there had been not any research in this field so far. Using the Delphi method in this research, out of the 24 indicators of the General Policies of Eghtesad-e-Moghavemati, 13 foreign exchange market-related indicators were recognized in the point of view of the expert. Using interpretive structural modeling and Excel software, the mentioned indicators were classified. The results show that the indicators of transparentization and correction of economy, and the indicator of coordination amongst the policies of Eghtesad-e-Moghavemati have high effectiveness, and the indicator of the knowledge-based economy, as the most infrastructural indicator, places at the last level.

Main question: taking into account the dependency of I.R.Iran to export of crowd oil and other petrochemical products, Iranian economics is highly sensitive toward foreign exchange market and it's turbulances.
The aim of this research is to identify elements that affect foreign exchange market and it's turbulence, and so, helps the policymakers in their decision making at national level.
Research

after a thorough literature review and conducting interviews with experts, we used thematic analysis to identify main effective elements in turbulence of foreign exchange market. Then we proceeded to use cross impacts between identified elements, by help of MICMAC software solution.

The main objective of the study is to investigate the bubble migration between foreign exchange and housing markets in Iran using seasonal data of 1966-1396. In this regard, bubble dating is discovered by using of Phillips et al (2015) method; then, bubble migration from the foreign exchange market to the housing market and from the housing market to the foreign exchange market is examined, using the Gomez-Gonzales et al. (2016) method. The results show that there are 7 bubble periods in the foreign exchange market; and 7 bubble periods in the Housing market. four hypotheses were defined for examine the bubble migration, according to the bubble dates (from the foreign exchange to housing market and vice versa). The results of the hypothesis testes showed that the first house bubble (2007Q2-2008Q3) led to the creation of the first bubble of the foreign exchange market (2007Q4-2008Q3). It seems that in the currency restriction conditions, the probability of creating a foreign exchange bubble and its migration to the housing market is rather high and vice versa.

In this study, the effect of global economic policy uncertainty on stock, gold and foreign exchange market volatility in Iran is examined. Therefore, using the monthly data of Tehran Stock Exchange price index, gold coin price and exchange rate for the period from April 2002 to March 2017, correlation of the mentioned variables with global economic policy uncertainty in Iran has been investigated by the dynamic conditional correlation-GARCH model (DCC-GARCH). The research results show that fluctuations in global economic policy have a significant effect on stock, gold coin and exchange rate market fluctuations. This index generally has a positive effect on the fluctuations of gold coin price (except during 2004-2007), and has both positive and negative effect on stock and exchange market fluctuations. Thus, the global economic uncertainty index is useful in predicting market volatility and can improve stock price, gold price and exchange rate forecasts.

Considering the price of the exchange rate and its volatility plays a significant role in financial decisions and economic transactions affected by large and small economic groups. In this study, we have tried to provide continuous modeling for exchange rate data in Iran with the support of a stochastic differential equation driven by the Levy process (that named continuous GARCH model) and check out the fitting exchange rate volatility on this model. Accordingly, we use the daily data of the unofficial exchange rate (the value of the US dollar against the Iranian Rial in the free market) from March 2009 to March 2018. We also challenge the performance of the models with time-varying volatility under the continuous features in comparison to the discrete GARCH model. Finally, according to investigating the efficiency of this model coinciding with the results of the measurement error criterion, the preference of the new continuous model is expressed.

The main objective of the study is to investigate the bubble contagion in two financial markets including the exchange and stock markets of Tehran during the period 2008-2018. First, the bubble occurrence in these two markets was dated by the use of the new methods of GSADF, SADF, and RADF. The results of this section showed that both markets were bubbling. The security markets had five periods of bubbles from 2010 to 2015, of which the fourth and the fifth bubbles were multiple. In the case of the foreign exchange market, the bubble occurred during the periods from 2011 to 2013 and from January to February 2018. Of those bubbles, only the third was multiple. In addition, the results confirmed the contagion of the second bubble in Tehran Stock Market to the foreign exchange market and the contagion of the third bubble of the foreign exchange market to Tehran Stock Market during the period under review. It was also found that the bubble contagion from the foreign exchange market to the stock market was more powerful than that from the stock market to the foreign exchange market.

Inferring and inducing among the relations for many of the phenomenas is common in most of sciences. Accordingly, econophysics experts are trying to fill the gap between microeconomics and macroeconomics to explain complex financial systems, using the statistical physics tools. Using a dynamic econometric model, the impact of exchange and gold markets shocks on the stock market is studied. The results show the more significant contribution of the foreign exchange market shocks than the gold market shocks on the fluctuations of stock market. Furthermore, this study forms an assumptive portfolio consisting of three assets including stock, exchange and gold and use random portfolio theory to analyze the risks and uncertainty. The level of uncertainty and risk is studied by using the characteristics of the Castaing, Gagne & Hopfinger distribution functions. The results indicate a high level of uncertainty and risk in the macroeconomic assets portfolio. In other words, the occurrence of financial crises in these markets is expected.