METHODS OF FORECASTING EXCHANGE RATES: ANALYSIS AND EVALUATION

DOI 10.31673/2412-4338.2025.026126

Abstract

 Annotation. The study examines key methods for forecasting exchange rates, including traditional approaches such as fundamental and technical analysis, as well as modern mathematical models, particularly ARIMA and GARCH. A comprehensive comparative analysis of the effectiveness of these methods under different economic conditions is conducted, taking into account the influence of macroeconomic indicators, political stability, global economic trends, and specific characteristics of local markets. It is shown that traditional approaches tend to lose their effectiveness under conditions of increased market volatility and crisis phenomena. This highlights the necessity of employing advanced data analysis methods, particularly machine learning algorithms, to enhance forecast accuracy and model adaptability.

Special attention is devoted to the development and implementation of a modified exchange rate forecasting method (MNKY), which combines classical econometric tools with artificial intelligence capabilities. The proposed method demonstrates high flexibility in model parameter adjustment, simplified integration of new data, and the ability to self-adapt to changing market conditions without compromising forecast quality. Experimental testing based on the UAH/USD exchange rate, using data from the National Bank of Ukraine, revealed that the average deviation of the MNKY method forecast was 2.5 units, significantly outperforming the ARIMA (8 units) and GARCH (4.8 units) models.

The study also reveals that integrating machine learning enables flexible consideration of the multifactor influence of political, economic, and social factors, traditionally complicating the forecasting of exchange rate dynamics. The scientific novelty of the study lies in substantiating the practical feasibility of hybrid methods and developing an optimized approach for short- and medium-term forecasting of currency trends.

The practical significance of the results is reflected in the potential application of the MNKY method by financial analysts, banking institutions, investment funds, and small and medium-sized enterprises for managing currency risks, developing anti-crisis strategies, and making informed financial decisions.

Additionally, the proposed approach can serve as a foundation for creating intelligent financial monitoring and forecasting systems, enhancing overall efficiency in currency and financial markets.

Keywords:  methods, ARIMA, GARCH, exchange rate, forecasting, fundamental analysis, technical analysis, time series, financial markets, volatility, machine learning.