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In order to determine which is the fixed currency when neither currency is on the above list (i.e.both are "other"), market convention is to use the fixed currency which gives an exchange rate greater than 1.000.The real exchange rate (RER) is the purchasing power of a currency relative to another at current exchange rates and prices.
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.
This report provides exchange rate information under Section 613 of Public Law 87-195 dated September 4, 1961 ( (b)) which gives the Secretary of the Treasury sole authority to establish the exchange rates for all foreign currencies or credits reported by all agencies of the government. The rates provided in this report are not meant to be used by the general public for conducting foreign currency conversion transactions.
The primary purpose is to ensure that foreign currency reports prepared by agencies are consistent with regularly published Treasury foreign currency reports regarding amounts stated in foreign currency units and U. This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process.
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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.