Diagnostics of USD/UAH Exchange Rate Dynamics under Floating Regime

Table of Contents

1. Introduction & Overview

This study conducts a comprehensive empirical analysis of the USD/UAH (Ukrainian Hryvnia) exchange rate dynamics following Ukraine's transition to a floating exchange rate regime and inflation targeting policy in 2014. The period from January 2014 to May 2020 is examined, characterized by macroeconomic imbalances, socio-political tensions, and significant currency volatility, including a low of 23.46 UAH/USD in December 2019. The research aims to diagnose whether the exchange rate movement follows a random or permanent trend, identify seasonal patterns, and evaluate its sensitivity to external macroeconomic shocks, thereby assessing the efficiency and stability of Ukraine's foreign exchange market.

2. Methodology & Data

The empirical analysis employs a robust suite of time series econometric techniques to test three central hypotheses concerning the nature of the USD/UAH exchange rate process.

2.1 Research Hypotheses

The study tests the following hypotheses: (H1) The USD/UAH exchange rate follows a stochastic (random walk) process rather than a deterministic trend. (H2) The dynamics exhibit statistically significant seasonal patterns. (H3) The exchange rate is sensitive to external macroeconomic shocks, but the Ukrainian forex market shows signs of relative efficiency if the reactions are short-term and mean-reverting.

2.2 Analytical Framework

A multi-method approach is utilized:

• Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron tests to determine stationarity and the presence of a stochastic trend.
• Autocorrelation Analysis: To identify patterns and persistence in the series.
• Granger Causality Tests: To explore lead-lag relationships between the exchange rate and key macroeconomic variables.
• Univariate Model: ARMA (AutoRegressive Moving Average) modeling for the trend-seasonal decomposition.
• Multivariate Model: Vector Autoregression (VAR) model and Impulse Response Functions (IRFs) to analyze the dynamic impact of shocks from various macroeconomic indicators on the exchange rate.

2.3 Data Period & Sources

Monthly data from January 2014 to May 2020 is used. The primary variable is the USD/UAH exchange rate. For the multivariate analysis, other macroeconomic indicators likely include inflation rates, interest rates, foreign reserves, trade balance figures, and possibly global factors like oil prices or the USD index, sourced from the National Bank of Ukraine (NBU) and other official statistical bodies.

3. Empirical Results & Analysis

3.1 Trend Analysis & Random Walk

The results from the ADF and Phillips-Perron tests indicate a failure to reject the null hypothesis of a unit root for the USD/UAH series within the sample period. This provides strong evidence for H1, suggesting the exchange rate movement is a stochastic process with a random walk component. The trend is not permanent but contains a random element, leading to sharp and unpredictable changes over time. This aligns with the weak-form Efficient Market Hypothesis (EMH) for the Ukrainian forex market, implying past price movements cannot reliably predict future changes.

3.2 Seasonality Detection

The analysis confirms H2, revealing a clear seasonal pattern in USD/UAH fluctuations. The Hryvnia tends to depreciate against the USD during the first and second quarters (Q1 & Q2) of the year and appreciate in the third and fourth quarters (Q3 & Q4). This pattern could be linked to cyclical factors such as agricultural export flows, corporate tax payment schedules, or seasonal demand for foreign currency.

3.3 Sensitivity to External Shocks

The VAR model and Impulse Response Functions show that the USD/UAH rate reacts to shocks from specific macroeconomic indicators, with reactions being either positive (depreciation) or negative (appreciation). Crucially, the study finds these reactions are short-term, statistically insignificant in magnitude, and exhibit a tendency to fade out over time. This supports H3 and suggests that while the market reacts to news (indicating relative efficiency), it is also stable as shocks do not cause persistent, destabilizing deviations.

4. Key Findings & Implications

• Stochastic & Unpredictable Trend: The USD/UAH rate follows a random walk, making precise short-to-medium term forecasting extremely difficult with linear models.
• Pronounced Seasonality: Policymakers and businesses can anticipate quarterly pressure points, though the random walk component limits exact prediction.
• Efficient but Thin Market: The quick, fading response to shocks indicates a market that incorporates information rapidly but may lack the depth to sustain large, prolonged movements from single shocks.
• Multifactorial Dependence: The exchange rate is influenced by several domestic and potentially global macroeconomic factors, consistent with standard international finance theory.
• Policy Challenge: For the National Bank of Ukraine, managing inflation under a floating regime with a highly volatile and stochastic exchange rate is a significant challenge.

5. Technical Details & Mathematical Framework

The core models are specified as follows:

Augmented Dickey-Fuller (ADF) Test:
$\Delta y_t = \alpha + \beta t + \gamma y_{t-1} + \sum_{i=1}^{p} \delta_i \Delta y_{t-i} + \epsilon_t$
The null hypothesis $H_0: \gamma = 0$ (unit root present). The study's results likely failed to reject $H_0$ for the level series.

Vector Autoregression (VAR) Model:
$\mathbf{Y}_t = \mathbf{A}_0 + \mathbf{A}_1\mathbf{Y}_{t-1} + ... + \mathbf{A}_p\mathbf{Y}_{t-p} + \mathbf{U}_t$
where $\mathbf{Y}_t$ is a vector containing the USD/UAH rate and other macroeconomic variables (e.g., inflation, interest rates), $\mathbf{A}_i$ are coefficient matrices, and $\mathbf{U}_t$ is a vector of white noise innovations.

Impulse Response Function (IRF):
Traces the effect of a one-standard-deviation shock to one variable (e.g., an inflation surprise) on the current and future values of all variables in the VAR system, particularly the USD/UAH rate: $\frac{\partial Y_{t+h}}{\partial u_{j,t}}$ for $h=0,1,2,...$

6. Experimental Results & Chart Descriptions

Figure 1 (Time Series Plot): Likely shows the nominal USD/UAH exchange rate from 2014-2020, highlighting the sharp depreciation in 2014-2015, relative stability in 2016-2018, and renewed volatility in 2019-2020, with the December 2019 peak.

Figure 2 (ACF/PACF Correlograms): Autocorrelation and Partial Autocorrelation Function plots used to identify ARMA model orders ($p$, $q$) and visually assess persistence (slowly decaying ACF suggests non-stationarity).

Figure 3 (Seasonal Decomposition): A plot decomposing the series into trend, seasonal, and residual components, visually confirming the Q1-Q2 depreciation / Q3-Q4 appreciation pattern.

Figures 4-7 (Impulse Response Functions): A series of charts showing the response of the USD/UAH exchange rate to orthogonalized shocks from other variables in the VAR (e.g., a shock to NBU policy rate, inflation, trade balance). The key observation is that the response paths hover around zero, with confidence intervals encompassing zero, indicating statistically insignificant and transient effects.

Tables 1-11: Present descriptive statistics, unit root test results (ADF/PP statistics and p-values), ARMA model estimation outputs, Granger causality test results (F-statistics and p-values), and VAR model estimation matrices.

7. Analysis Framework: A Practical Case

Scenario: A Ukrainian agricultural exporter wants to assess FX risk for revenues due in June 2024.

Framework Application:

1. Trend Component (Stochastic): The analyst acknowledges the random walk nature. A point forecast from an ARMA model is highly uncertain. Instead, they focus on forecasting the distribution of possible outcomes (e.g., using a Geometric Brownian Motion simulation: $dS_t = \mu S_t dt + \sigma S_t dW_t$, where $S_t$ is the exchange rate).
2. Seasonal Adjustment: Historical data shows June (Q2) is typically a period of Hryvnia weakness. The analyst would factor in a seasonal depreciation bias into their risk model, perhaps by analyzing average June returns over the past 10 years.
3. Shock Analysis: Using a simplified version of the paper's VAR framework, the analyst monitors leading indicators (e.g., monthly inflation prints, NBU commentary, global USD strength). The IRF logic tells them that even a "bad" inflation number should not cause a permanent shift if the market is efficient, but it could cause short-term volatility.
4. Hedging Decision: Given the high volatility (stochastic trend) and seasonal headwind, the analyst recommends hedging a significant portion of the expected June revenue via forward contracts or options, rather than leaving it unhedged based on a naive forecast.

8. Future Applications & Research Directions

• Non-Linear & Machine Learning Models: Given the limitations of linear models (ARMA, VAR) in predicting a random walk, future research should employ non-linear models like GARCH for volatility clustering, or machine learning techniques (LSTM networks, Random Forests) to capture complex, non-linear dependencies that might offer improved predictive power for risk management, as seen in advanced forex forecasting studies (e.g., experiments combining LSTM with attention mechanisms).
• High-Frequency Data Analysis: Using intraday or tick data to test market micro-structure and the speed of adjustment to news, providing a sharper test of market efficiency.
• Integration of Global Risk Factors: Explicitly incorporating global variables like the ICE U.S. Dollar Index (DXY), VIX (volatility index), or commodity prices into the VAR model to disentangle domestic from global drivers.
• Policy Evaluation: Using the established framework as a counterfactual to evaluate the impact of specific NBU interventions or policy changes post-2020.
• Application to Crypto-Fiat Pairs: The methodology could be adapted to analyze the dynamics of emerging market currencies against cryptocurrencies, a growing area of interest in decentralized finance (DeFi).

9. References

1. Ignatyuk, A., Osetskyi, V., Makarenko, M., & Artemenko, A. (2020). Ukrainian hryvnia under the floating exchange rate regime: diagnostics of the USD/UAH exchange rate dynamics. Banks and Bank Systems, 15(3), 129-146.
2. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.
3. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
4. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.
5. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48.
6. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
7. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
9. National Bank of Ukraine. (2024). Official statistics and reports. Retrieved from [NBU Website].
10. International Monetary Fund. (2023). Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER).

10. Analyst's Perspective: Core Insight, Logical Flow, Strengths & Flaws, Actionable Insights

Core Insight: This paper delivers a cold, hard truth for anyone betting on the Hryvnia: its core trend is fundamentally unpredictable. The authors convincingly demonstrate that the USD/UAH rate is a classic random walk, burying the hope of reliable linear forecasting models. The real kicker is the coexistence of this chaos with clear seasonal patterns and a market that digests news efficiently but briefly. This paints a picture of a market that is mechanically efficient but fundamentally unstable—a dangerous combination for long-term investors but a potential playground for tactical, seasonality-aware traders.

Logical Flow: The argument is methodical and robust. It starts with a clear hypothesis (random walk), uses industry-standard tests (ADF, PP) to confirm it, then layers on complexity by identifying seasonal anomalies that the random walk doesn't preclude. Finally, it uses a VAR model to stress-test the market's resilience, finding it absorbs shocks quickly—the hallmark of a reasonably efficient, if not deep, market. The flow from univariate to multivariate analysis is textbook and effective.

Strengths & Flaws: The strength is in the comprehensive methodological toolkit and the clear, data-driven conclusions. The authors don't overreach. However, the major flaw is one of omission in the modern context: the complete absence of non-linear or machine learning approaches. Sticking to ARMA/VAR in 2020 to analyze a volatile EM currency is like using a map to navigate a hurricane. Studies like those applying LSTMs to forex (e.g., Sezer et al., 2020) show significant gains in capturing the complex patterns a random walk might mask. Furthermore, the "external shocks" are likely too domestically focused, missing the elephant in the room: the overarching influence of the US Federal Reserve's policy and global dollar cycles on a dollarized economy like Ukraine's.

Actionable Insights:

• For Corporates & Banks: Abandon point forecasts for operational planning. Immediately shift to probabilistic scenario analysis and stress-testing. Use the identified Q1/Q2 seasonality as a systematic factor in your annual hedging calendar—consider layering in more protection during these windows.
• For the NBU: The findings validate the extreme difficulty of inflation targeting with a floating, random-walk currency. Communication strategy must emphasize managing expectations and volatility over attempting to steer the level. Consider publishing a "seasonal factors" addendum to inflation reports to anchor public understanding.
• For Researchers: This paper is a perfect baseline. The next step is to supersede it with models that can handle the non-linearity this study hints at. Partner with data science teams to apply gradient boosting or neural networks to this same data set; the comparison of results would be highly publishable.
• For Investors: Treat Ukraine as a high-volatility, tactical allocation. The seasonal pattern (weak H1, strong H2) offers a potential, though risky, systematic tilt. Any long-term position must be premised on fundamental reform improving the underlying drivers of volatility, not on currency prediction.