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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Creative Commons Attribution 4.0 International License
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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- Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and Momentum Everywhere. The Journal of Finance, 68(3), 929-985.
- Bartram, S. M., Djuranovik, L., & Garratt, A. (2018). Currency Anomalies. 31st Australasian Finance and Banking Conference.
- Boothe, P., & Longworth, D. (1986). Foreign exchange market efficiency tests: Implications of recent empirical findings. Journal of International Money and Finance, 5(2), 135-152.
- Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry Trade and Momentum in Currency Markets. Annual Review of Financial Economics, 3, 511-535.
- Colacito, R., Riddiough, S., & Sarno, L. (2019). Business Cycles and Currency Returns (NBER Working Paper No. w26299).
- Corte, P. D., Ramadorai, T., & Sarno, L. (2014). Volatility Risk Premia and Exchange Rate Predictability. Journal of Financial Economics, 120(1), 21-40.
- Dahlquist, M., & Hasseltoft, H. (2015). Economic Momentum and Currency Returns. Stockholm School of Economics and CEPR.
- Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
- Hansen, B. E. (2020) Econometrics (896 p.). University of Wisconsin Department of Economics.
- Kallianiotis, I. N. (2018). How Efficient is the Foreign Exchange Market? Athens Journal of Business & Economics, 4(3), 293-326.
- Levich, R. M. (1989). Is the foreign exchange market efficient? Oxford Review of Economic Policy, 5(3), 40-60.
- Maurer, T. A., To, T. D., & Tran, N. K. (2019). Pricing Risks across Currency Denominations. Management Science, 65(12).
- McLean, R. D., & Pontiff, J. (2012). Does Academic Research Destroy Stock Return Predictability? The Journal of Finance, 71(1), 5-32.
- Meese, R. A., & Rogoff, K. (1983). Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample? Journal of International Economics, 14(1-2), 3-24.
- National Bank of Ukraine (NBU). (2020). Macroeconomic Indicators, Financial Sector Statistics.
- National Bank of Ukraine (NBU). (2020, April). Infliatsiinyi zvit [Inflation report]. (In Ukrainian).
- National Bank of Ukraine (NBU). (2020, February 25). Rishennia “Pro diialnist Pravlinnia Natsionalnoho banku Ukrainy shchodo vykonannia Osnovnykh zasad hroshovo-kredytnoi polityky v 2019 rotsi” [Decision “On the activities of the Board of the National Bank of Ukraine to implement the Basic Principles of Monetary Policy in 2019”]. (In Ukrainian).
- Oh, G., Kim, S., & Eom, Ch. (2007). Market efficiency in foreign exchange markets. Physica A: Statistical Mechanics and its Applications, 382(1), 209-212.
- Pieńkowski, J. (2020). The Impact of Labour Migration on the Ukrainian Economy (European Economy Discussion Papers No. 123). Luxembourg: Publications Office of the European Union.
- State Statistics Service of Ukraine. (2020). Economic statistics.
- The Organization of the Petroleum Exporting Countries (OPEC). (2020). OPEC Basket Price statistics data.
- United Nations (UN). (2019). International Migration 2019 Report. Department of Economic and Social Affairs of United Nations.
- Zhuravka, F., Makarenko, M., Osetskyi, V., Podmarov, O., & Chentsov, V. (2019). Impact of politically generated shocks on monetaryperformance: a cross-country comparison. Banks and Bank Systems, 14(3), 99-112.
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