EXCHANGE RATE PREDICTION, as one of the main variables in macroeconomics, has been one of the aims of the economic research for a long time. For modeling and predicting EXCHANGE RATE we apply stochastic differential equation, specifically we use Geometric Brownian Motion (GBM) and Jump-Diffusion process (MJDP) attributed to Merton. We show that the result of simulation based on GBM and MJDP outperforms linear time series models, such as ARIMA, for both in sample and out of sample PREDICTIONs based on RMSE criterion.

Given the high EXCHANGE RATE fluctuations in Iran, its PREDICTION is one of the major issues and challenges for different groups in the country. This study investigates the performance of six different static and dynamic neural networks for forecasting EXCHANGE RATEs using fundamental, technical and hybrid approaches and using seasonal data over the period 2004(1)-2018(4) for variables influencing EXCHANGE RATE including inflation, Liquidity and GDP for the two countries, Iran and the United States. The findings show that the number of neurons did not have a regular effect on the performance of the networks and that the best results occurred at breaks of three and four. The results also show that the best performance of the static neural network is achieved by a technical approach with a structure of sixteen neurons and four interruptions which provides a relatively accuRATE EXCHANGE RATE PREDICTION despite the low number of input data. The second suitable function is related to the combined static network with the structure of ten neurons and two interruptions. With this in mind, policymakers can, given the greater and more up-to-date access to data affecting the EXCHANGE RATE and by monitoring the instantaneous variables and entering them into the comprehensive model designed using this method, the RATE of EXCHANGE RATE deviation Examine the existing model and EXCHANGE RATE and adopt appropriate policies based on this, so that the losses on the domestic and foreign sectors of the economy are due to the predicted RATE gap and the current EXCHANGE RATE is at a minimum.

This research examines and analyses the use of Artificial Neural Network (ANN) models in foreign EXCHANGE (FX) forecasting and trading models. Also, the performance of five linear models of EXCHANGE RATE of Iranian Rial/US dollar is compared with that of ANNs. The linear models are Box-Jenkins methods (auto regressive integRATEd moving average), two forms of random walk process (with a variable drift and without it) and three different specifications based on the purchasing power parity (PPP) hypothesis. The aim is to examine whether potentially highly nonlinear neural network models outperform traditional methods?We employ ANNs to study the nonlinear predictability of EXCHANGE RATE for the Iranian Rial /US dollar. The comparative exercise has been conducted both in-simple and out-of-simple, at the 1-, 6- and 12-month forecast horizons. In general, the results confirm the difficulty in forecasting EXCHANGE rats, and reaffirm those obtained in previous literature which shows that the performance of econometric models of the EXCHANGE RATEs is inferior to that of a random walk (RW). In the direct comparison between structure econometric, time series and artificial neural network, the experiment with monthly data indicates that there ANNs clearly improves forecasting the EXCHANGE RATE.

The effectiveness of monetary shocks on EXCHANGE RATE fluctuations in the context of overshooting theory is one of the most important subjects in macroeconomic. Since the economy of Iran after revolution years has exposed to monetary base expansion; so' examination of the relation between expansionary monetary policy and EXCHANGE RATE fluctuations and then the role of increasing the degree of floating EXCHANGE RATE with increasing of this fluctuation, is the principal purpose of this research. Therefore, in the first section of the research, the effectiveness of a monetary shock on EXCHANGE RATE during the period of 1370-1386 has examined. In the overshooting EXCHANGE RATE models form was seen, after a monetary shock, EXCHANGE RATE shoots over the long run level and in the long run, is adjusted and stands in the level that is over the early level. In the second section research, after the PREDICTION of the EXCHANGE RATE market by the neural networks, it was seen, by increase the degree of floating EXCHANGE RATE, the scope of EXCHANGE RATE overshooting increases.

Understanding the identity of phenomenon and their relation is one of the function of science. the insight of this knowledge can help scientists to predict the future and make the background of changes and decisions.In addition to understanding of phenomenon relationship some other studies try to compare the theories and find the best one on the point of their predictability. In this study we tried to review different paradigm and thoughts and theories of EXCHANGE RATE and its behavior and also to compare their ability in forecasting the behavior of EXCHANGE RATE, for this reason we postulate four theories of EXCHANGE RATE which are: mondell-flemming theory, purchasing power parity thory, asset market theory and monetary with flexible price.Our time framework include "in the sample" and "out of sample" data and our case study in this research is examining the behavior of GBP/USD in foreign EXCHANGE market (FOREX), in the sample data include 01/01/1988 to 01/06/2008 monthly and out of sample data include 01/01/2006 to 01/06/2008.In this research we postulated the theories with in the sample data and after examining the validity of model with macro econometric techniques, then with extracting the four measures RMSE, MAPE, MAE and THEIL INEQUALITY COEFFICIENT we evaluated the ability of forecasting each model based on its theory. Finally the result of the study showed that in the mentioned time framework mondell-flemming model could forecast the behavior of GBP/USD better than other theories.

This paper intends to draw the relation between real EXCHANGE RATE variability and the choice of EXCHANGE RATE regime. The hypothesis was tested by estimating a simultaneous Limited-Dependent variable model with data from a time series during 1973-1996. The paper examines a number of exogenous determinants of EXCHANGE RATE variability and the choice of EXCHANGE RATE regime.Our results indicate that: 1) The more opening in Iran's economy, the greater in REER variability, 2) An increase in domestic monetary shocks will result in greater REER variability, 3) REER variability decreases in the fixed EXCHANGE RATE regime, 4) As an openness increases, the choice of the floating regime is more suitable, 5) The greater REER variability, the more likely a fixed EXCHANGE RATE.

In this study, the effect of EXCHANGE RATE fluctuations on abnormal returns of companies listed on the Stock EXCHANGE were studied. In this study, Auto Regressive Distributed Lag model due to its ability to explain this connection was used for short-term and long-term. To evaluate the effect of EXCHANGE RATE fluctuations, the banks' legal deposit at the central bank, GDP, inflation, current account and capital account on the stock market according to the designated filters, exporting companies that gained the research conditions, were determined. In this study, the company's export price index was calculated at the end of each quarter and abnormal returns were calculated for the group of companies. After calculating abnormal returns of firms, independent and control variables in the ARDL model imported and the effects of the explanatory variables investigation revealed abnormal returns. The results show that the EXCHANGE RATE fluctuations variable has a positive and significant impact on exporting companies are abnormal returns.

According to the theory of macroeconomics, achieving stable equilibrium in economy is possible when EXCHANGE RATE regimes are consistent with the financial and monetary policies. Besides, regulating the real RATE of EXCHANGE and its relation to a known EXCHANGE RATE regime, which corresponds to economic conditions, is very important to create the equilibrium This study tries to forecast an EXCHANGE RATE which can guarantee the growth of non-oil exports, by emphasizing on making EXCHANGE RATE regime consistent with the EXCHANGE RATE on the one hand and making financial and monetary policies harmonious with devaluation of  Rial on the other hand. To this end, we have employed a VAR model to determine an appropriate EXCHANGE RATE to forecast the behavior of EXCHANGE RATE and other related variables over 1996-2000 (1375-79) period. In this framework, a model is worked out to can simultaneously specify the effects of financial policies, liquidity growth, inflation, and devaluation of Rial on non-oil exports within a five-year period. For this purpose, a model is presented with five endogenous and four exogenous variables. The results show that an increase in inflation cancels out the positive effects of devaluation on non-oil exports. Rial devaluation will boost the growth of non-oil exports if it is accompanied by appropriate financial policies. Therefore, if the control of government expenditures is hinged with the devaluation of Rial, it may lead to a reduction in the growth of liquidity and inflation as well as to the growth of non-oil exports.