Ukrainian hryvnia under the floating exchange rate regime: diagnostics of the USD/UAH exchange rate dynamics
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Author(s)Anzhela IgnatyukLink to ORCID Index: https://orcid.org/0000-0002-9103-6943
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Valerii OsetskyiLink to ORCID Index: http://orcid.org/0000-0002-8144-5313
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Mykhaylo MakarenkoLink to ORCID Index: https://orcid.org/0000-0002-6357-5317
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Alina ArtemenkoLink to ORCID Index: https://orcid.org/0000-0002-7880-7360
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DOIhttp://dx.doi.org/10.21511/bbs.15(3).2020.12
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Article InfoVolume 15 2020, Issue #3, pp. 129-146
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The study identifies the features of the USD/UAH exchange rate dynamics for the period from January 2014 to May 2020. The main purpose of the empirical analysis is to determine the current trend of the USD/UAH exchange rate (is it random or permanent), indicate the presence of seasonality in foreign exchange rate dynamics and evaluate its sensitivity to external shocks. Three hypotheses are tested using several methods of time series analysis (autocorrelation analysis, ADF, Phillips-Perron and Granger tests), including a trend-season model using a time series of one variable (ARMA), a multifactor VAR-model, impulse functions. The results show that, the movement of the hryvnia exchange rate against the US dollar is a stochastic process. Its trend has a random component and tends to change sharply over time. Moreover, exchange rate fluctuations are seasonal. It depreciates in the first and second quarters, and strengthens in the third and fourth. Some macroeconomic indicators cause a positive or negative reaction of the USD/UAH exchange rate. This indicates that today the Ukrainian foreign exchange market is relatively efficient, but stable, since its reaction to external shocks is short-term, insignificant and tends to fade out. Although the findings are controversial, they support the generally accepted view that the exchange rate formation is a multifactorial process that depends on several macroeconomic factors. However, high volatility and random walk specification indicate that it is almost impossible to predict its future value at this time.
Acknowledgment
The material was prepared within the framework of the scientific research Modeling and Forecasting the Behavior of Financial Markets as an Information Base for Ensuring Financial Stability and Security of the State, No. 0117U003936 (supervisor Alex Plastun).
- Keywords
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JEL Classification (Paper profile tab)E59, F31, F41
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References23
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Tables11
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Figures7
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- Figure 1. Dynamics of the USD/UAH official exchange rate, 2000 – May 2020
- Figure 2. Dynamics of the USD/UAH official exchange rate and CPI, 2008 – May 2020
- Figure 3. Autocorrelation diagrams, USD/UAH exchange rate, 2014 – May 2020 (left side – in absolute values, right side – in first differences)
- Figure 4. Current and forecast values of the USD/UAH exchange rate, calculated based on VAR (2) model
- Figure B1. USD/UAH official exchange rate forecast, 3rd and 4th quarters of 2020 (additive model)
- Figure B2. USD/UAH official exchange rate forecast, 3rd and 4th quarters of 2020 (multiplicative model)
- Figure D1. Impulse response functions for the VAR (2) model
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- Table 1. Dickey-Fuller test for unit root (USD/UAH official exchange rate)
- Table 2. Phillips-Perron test for unit root (USD/UAH official exchange rate)
- Table 3. Trend equations of multiplicative and additive models (forecast for the 3rd and 4th quarter of 2020)
- Table 4. Results for the lag structure test of the VAR-model
- Table 5. Granger causality test results for VAR (2) model factors
- Table 6. Impulse reaction for VAR (2) model, %
- Table A1. Autocorrelation coefficients, USD/UAH exchange rate, 2014 – May 2020 (in absolute values and in first differences)
- Table B1. Estimation of the seasonal component of the additive and multiplicative models of the USA/UAH exchange rate time series, 2014 – May 2020
- Table B2. Calculations of forecast values for additive and multiplicative models
- Table C1. Dickey-Fuller test results for all indicators of the VAR-model (suppress constant specification)
- Table C2. VAR (2) model results (standard OLS regression summary statistics)
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