Forex markets are among the most active markets in the world in terms of dollar volume.
The participants include large banks, multinational corporations, governments, and speculators.
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The forward exchange rate refers to an exchange rate that is quoted and traded today but for delivery and payment on a specific future date.
In some areas of Europe and in the retail market in the United Kingdom, EUR and GBP are reversed so that GBP is quoted as the fixed currency to the euro.
Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day.
To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network.
If all goods were freely tradable, and foreign and domestic residents purchased identical baskets of goods, purchasing power parity (PPP) would hold for the exchange rate and GDP deflators (price levels) of the two countries, and the real exchange rate would always equal 1.
The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average.
The moving average is supposed to enhance the outputs of the network using the error part of the original neural network.
They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm.
Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. Profits or losses accrue as the exchange rate of that currency fluctuates on the open market.
In order to determine which is the fixed currency when neither currency is on the above list (i.e.