Abstract
The paper contains an analysis of advantages and drawbacks of existing modeling approaches to exchange rate forecast and currency risk management. The behavioral features of foreign exchange market participants and their impact on market’s long-term memory were analyzed after the onset of significant events. Special attention was paid to the “Pareto distribution series” to find the point values of the rates that form the trends and determine the trajectory of currency rates. Based on the existing researches of FX rate prediction models authors developed an alternative approach that gives more accurate forecasts of the FX rates and, respectively, currency risk assessment in national banking systems.
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Notes
Suppose, X- is an integrated times series. We subject this series to k-fold differentiation. If as a result we get a stationary time series of ARMA (p, q) type, then we say that the original series X is ARIMA (p, k, q), or integrated once ARMA (p, q) (ARIMA—autoregressive integrated moving average). If p = 0 or q = 0, and then use a short notation (Nosko 2000).
Stationary time series must meet the following criteria: a constant variance, a constant mathematical expectation, a permanent structure of the correlogram.
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Sandoyan, E., Manukyan, D. Exchange Rate Forecast: a New Approach for Armenian Dram. Transit Stud Rev 20, 159–177 (2013). https://doi.org/10.1007/s11300-013-0283-5
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DOI: https://doi.org/10.1007/s11300-013-0283-5