ECONOMIC AND MATHEMATICAL TOOLS FOR PREDICTING THE CURRENCY EXCHANGE RATE

• Published in: Scientific opinion: Economics and Management
• Main Authors: Melnyk, Ostap, Novoseletskyy, Oleksandr
• Format: Journal Article
• Language: English
• Published: 2022
• ISSN: 2521-666X; 2706-9079
• DOI: 10.32836/2521-666X/2022-78-24

The article deals with the analysis of existing approaches to exchange rate forecasting. It also includes the review of Ukrainian and foreign scientists on this topic. The authors of this article have considered the main disadvantages and benefits of existing forecasting dimensions, as well as individual methods and models. They indicated ways to facilitate the implementation of currency exchange rate forecasting using neural networks with software libraries for various programming languages and individual software applications, as well. As a result, the authors have systematized knowledge about existing approaches used in the process of currency exchange rate forecasting. There are two dimensions of currency exchange rate forecasting, in particular, intuitive and formalized ones. The intuitive dimension is peculiar to short-term forecasting and is often used in trading. Its main advantages include the ability to consider structural changes in the economy that can significantly affect the exchange rate formation itself and the speed of forecasting. However, the disadvantage of intuitive methods is the inability to prove formally the quality of the obtained forecasts. The advantages of the formalized dimension of forecasting include the ability to prove the quality. Businesses and government agencies use it the most often. Extrapolation methods and machine learning methods are mainly used to predict the exchange rate using formalized methods. Moreover, the reviewed studies indicate that among the well-known extrapolation methods for predicting the exchange rate, autoregressive models (VAR, AR, ARMA, ARIMA, SARIMA, ARCH, GARCH, ARDL) and smoothing methods (floating averages, adaptive methods and models) are used the most frequently. Machine learning methods include neural networks. Trend models have proved to be ineffective for currency exchange rate forecasting. The reason for this appeared to be using large amounts of data for currency exchange rate forecasting, and each fluctuation there directly affects the whole phenomenon.