The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable ...

Volatility of exchange rate while changes from time to time, is expected to affect firm level operations as well as aggregate level outcomes i.e. macroeconomic performance. This paper, investigates the effects of exchange rate volatility on aggregate production in Iran using a Structural Vector Auto Regressive model with Exogenous Variables (SVARX). The model is estimated based on macroeconomic...

this paper analyzes the effects ofexchange policies on iran,s foreign trade, by applying new econometric techniques. in assessing the impacts of depreciation of the national currency on macroeconomic variable, the real rather than nominal exchange rate has been determined to be more appropriate criterion to demonstrate the success or failure ofthe adopted policies. during the period under the s...

This paper presents the application of six nonlinear ensemble architectures to forecasting the foreign exchange rates in the computational intelligence paradigm. Intelligent techniques such as Backpropagation neural network (BPNN), Wavelet neural network (WNN), Multivariate adaptive regression splines (MARS), Support vector regression (SVR), Dynamic evolving neuro-fuzzy inference system (DENFIS...

This paper presents a new option that can be used by agents for managing foreign exchange risk. Unlike the Garman Kolhagen model [1], (GK), this paper presents a new model with a preset exchange rate (PE), that allows the agent to take advantage of the his/her view on both the direction and magnitude of rate movement and as such provides this agent with more choices. The model has a provision f...