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There are various ways to measure RER. Thus the real exchange rate is the exchange rate times the relative prices of a market basket of goods in the two countries.For example, the purchasing power of the US dollar relative to that of the euro is the dollar price of a euro (dollars per euro) times the euro price of one unit of the market basket (euros/goods unit) divided by the dollar price of the market basket (dollars per goods unit), and hence is dimensionless.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.You can convert currencies and precious metals with this currency calculator.Eur/Usd · USD - Us Dollar · GBP - British Pound · Cad/Usd · Currency Charts Currency Converter | Foreign Exchange Rates | OANDA https:// Free currency converter or travel reference card using daily OANDA Rate® data. Find the latest currency exchange rates and convert all major world currencies with our currency converter.
Because of the volatility in the price of foreign currency, losses can accrue very rapidly, wiping out an investor’s down payment in short order.
The real exchange rate (RER) is the purchasing power of a currency relative to another at current exchange rates and prices.
It is the ratio of the number of units of a given country's currency necessary to buy a market basket of goods in the other country, after acquiring the other country's currency in the foreign exchange market, to the number of units of the given country's currency that would be necessary to buy that market basket directly in the given country.
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.