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

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.

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. 

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.

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.

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.

The asset price bubble is the deviation of the asset price from its fundamental value. The price bubbles usually have common features. On one hand, providing excessive loans and facilities along with continuous raises in demand and asset price cause an inflation in price bubbles. On the other hand, the bubbles burst and the asset prices collapse because of abundant sales. Since many of the financial crisis arise from bursting bubbles of financial assets, exploring the bubbles behavior in these markets and the early detection for the prevention of adverse economic consequences is important.
Generally, the correct detection of assets price bubbles is complex and difficult due to uncertainty in accurate identification of the fundamental factors and their randomness. Especially, if the bubbles were inherent and dependent on fundamentals, the correct specification of the bubble equation will be more difficult. Many of the methods used for testing the price bubbles, have been criticized for various reasons. In this line, the present study has used Sigma-Point Kalman Filter (SPKF) to measure the price bubbles in the non-official foreign exchange market of Iran during the period of 2002:04-2015:09, forming a nonlinear state space model. In this paper, to derive the state-space model, the presented value model with variable discount rates was used as a start point. After solving this equation, the asset price includes two components, the fundamental value and the non-fundamental value or the speculative bubble. By applying the transversality condition, we obtain no bubble solution. The fundamental value is affected by market fundamentals and is expressed as the sum of the asset’s expected future dividends discounted to the present time. But, if the transversality condition be violated, this equation is the only solution for main equation.
The price bubble can be a function of fundamental factors. In addition, the relationship between the expected bubble and the current bubble values follow a nonlinear process.
If prices and fundamentals have unit root, we can write the general solution in differences. Since the bubble is an unobservable component, we regard it as a state vector and estimate it in a state space model. In addition, because fundamentals and price are observable, we consider them as observation vectors in signal equation. We assume the fundamentals follow an ARIMA (h, 1, 0) process, where h is determined empirically.
In the present study, we apply the Sigma Point Kalman filter (SPKF) for foreign exchange market in Iran. In this regard, we have taken consumer price index and real interest rate as fundamentals in nonofficial exchange rate of Iran. After specifying the state space model, this model is estimated using the maximum likelihood method. Then, we extract the bubble component using SPKF algorithm and by the MATLAB software. In order to identify the series of foreign exchange market bubbles, we have followed the SPKF methodology to detach the bubble component from the fundamental component in exchange rate. Since price variables are nonstationary in level, but stationary in first difference, we are capable of separating the bubble component of a change in the exchange rate from its fundamental components.
Regarding SPKF model estimation for the period of 2002:04 to 2015:09, most of the bubbles in exchange rate changes have been compressed at the 2012m09 so that about 61 percent of the exchange rate change can be attributed to the bubble component. Moreover, it can be seen that the non-official exchange rate of Iran in the 2000s had a very mild rising trend and slight volatilities. However, in the 2010s, at the same time with the intensification of economic sanctions, the decline in foreign exchange earnings and the rise in inflation, the exchange rate has experienced a significant growth. In this paper, we applied a Sigma-Point Kalman filter (SPKF) to extract the bubble term from exchange rate changes in non-official market of Iran during 2002:04-2015:09.The results showed that the significant part of the exchange rate changes, especially during 2010s, have been due to the formation of price bubbles. In this framework, the highest share of bubbles in exchange rate changes has been related to 2012:10 so that about 61 percent of the increase in exchange rate in this month compared to the previous month have been attributed to price bubbles. Moreover, in 2015, the exchange rate bubbles have been very insignificant and the market value of the exchange rate has been close to its fundamental value

Government has complicated multi-dimensional role in Economics. The effect of its policies would not be restricted inside the targeted market but changes the equilibrium and resource allocation on all other markets and, hence, the consistency of government policies is extremely important. In the literature although the role of government has been discussed hugely, however, the interrelation and dependency between government policies has not been considered as it weights. This article using structural equations modeling tries to analyze various dimensions of government interventions and determine the interrelation between its policies on different markets. Focusing on monetary, credit and foreign exchange markets, the results indicate a substitutive relation between interventions on credit and foreign exchange markets and complement relations between these two with money market. The substitutive relation between credit and foreign exchange market means that government has intervened in both to control external shocks and protect domestic production. But the effect of interventions is not restricted inside these two markets. The results show that monetary policies of government have been passive. In the last three decades government interventions in exchange and credit market have determined and leaded government policies in monetary market.

The aim of this paper is modeling the direct effects of sanctions on Iranian exchange market and its overflow impacts on macroeconomic variables including inflation and unemployment during the 1978-2015 period. For this purpose, a diverse range of econometric models such as ARMAX, GARCH and Markov Switching have been applied. The estimation results of the research models have indicated that the sanctions have three direct effects on exchange market including: increasing exchange rate, increasing the gap between official and free market exchange rates and increasing exchange rate volatility. Also, the estimation results of the Markov Switching models have shown that the sanctions have increased both of inflation and unemployment rates as a way of foreign exchange market. In other words, increasing the real exchange rate, increasing the gap between official and free market exchange rates and increasing the exchange rate volatility have a significant positive impact on both of unemployment and inflation rate.

Today, many researchers and international financial institutions are interested in measuring and identifying the dynamic interdependency among different financial market and their volatility changes. Index. In order to examine the spillover effect of the foreign currency market on the domestic monetary and financial system in Iran, this paper investigates volatility spillover of foreign currency exchange rates (like dollar and euro to rial) on the banking sector index in the stock market. In this research, we use logarithmic returns and apply them in multivariate GARCH models (in four different methods) for the twelve years periods (from April 2005 to March 2017). This paper is aimed to identify the effect of foreign currency market fluctuations on the banking sector index in order to help to manage financial fluctuations, policy-making and risk management decisions. The results indicate a positive conditional correlation between the short-term volatility of dollar exchange rate and long-run volatility of euro exchange rate with the banking sector index. Moreover, our findings confirm the existence of spillover effects of exchange rate on the banking sector index.

This paper analyzes the dynamic relationship between housing market, stock market’s           general index and real effective exchange rate of Iran using VAR and MGARCH models. For this purpose, monthly data of time period between Farvardin 1383 till Ordibehesht 1395 (Persian calendar) have been used. Based on the obtained results, there is no significant effect of other markets’ returns on housing market returns, while there is a significant and negative effect of stock market and housing market returns on foreign exchange market returns. In addition, in this study, the effect of simultaneous fluctuations of the housing market, foreign exchange and stock markets have also been evaluated. The results show that each market is not independent from other markets and a single market fluctuations will affect on the other markets. Because of the degree of simultaneous fluctuations among three markets, in order to make decision in one market and reduce the errors in decision making, policy makers can also consider political tools in other markets. Furthermore, investors may allocate their assets to these three markets in order to reduce the risk of investment

Fluctuations inforeignexchange ratesagainstthe national currecyis one of themain factorscausinguncertaintyanddisordersas a result ofthe currency market. Therefore, countries using the futures Exchange, the price and sales with them ethod that will beex changed in the future, the management and control offoreign exchange fluctuation shave. But according to the trading religion against religion based on Shia jurisprudence, is illegitimate, ýthis method can be used to manage exchange rate fluctuations in our country and only if all ýconditions are met futures contract, take advantage of it exist. On the other hand, high capacity ýutilization of financial tools, based on possible legal contract management and foreign ýexchange fluctuations to provide the monetary authorities. So can "ý Future's Sukuk of Exchange" is one of the financial tools.
This article is a descriptive - analytical method and use valid library has tried to prove the ýhypothesis that "theý Future's Sukuk of Exchange A tool in order to adjusting foreign exchange market fluctuations, liquidity ýcontrol and speculative behavior in the currency market is" and concluded Exchange futures can be ýconcluded that the sukuk to control three major foreign exchange market turmoil, the grammatical ýfixed exchange rate by the central bank, the volume of liquidity and speculative behavior, be used.ý

This paper investigates the impact of introduction of gold coin futures market on the volatility of the underlying gold coin cash market in Iran. The data used in this analysis incorporates the time period starting from 2 years prior to, until 5 years after the introduction of gold coin futures market (i.e. - 2006 to 2013) with a total number of 1.846 observations. The GARCH Model is used in order to investigate the impact of gold coin’s futures trading on the volatility of gold coin’s cash market in Iran. The empirical results suggest that the introduction of gold coin’s futures market has increased the volatility of gold coin’s cash market in Iran.

The main purpose of this paper is theoretically to design the optimal intervention in the foreign exchange market in Iran, which has not been investigated by previous studies. The economic fundamentals are strongly affected by the optimal foreign exchange intervention, the consistency of foreign exchange intervention with other policies and political authoritie's beliefs about the intervention as a dependent policy instrument. The used approach in this paper is a nonlinear dynamic continuous-time stochastic model. The result shows that the optimal foreign exchange intervention depends on the economic objectives of policy authorities, current and expected future exchange rate.