Diagnostics of USD/UAH Exchange Rate Dynamics under Floating Regime

Table of Contents

1. Introduction & Overview

Wannan bincike ya gudanar da cikakken bincike na zahiri na farashin musayar kudin Ukrainian Hryvnia (UAH) da Dalar Amurka (USD) a karkashin tsarin farashin musayar kudin da aka yi amfani da shi a shekarar 2014. Lokacin daga Janairu 2014 zuwa Mayu 2020 ya kunshi gagarumin tashin hankali na tattalin arziki da siyasa ga Ukraine, gami da sauyi bayan Maidan, rikici a gabas, da kuma karkata zuwa manufar rage hauhawar farashin kayayyaki. Babban manufar ita ce rarraba yanayin farashin USD/UAH don tantance ko motsinsa yana bin tafiya bazuwar, yana nuna alamu na yanayi, da kuma yadda yake mayar da martani ga tasirin tattalin arzikin duniya na waje. Sakamakon binciken yana nufin tantance inganci da kwanciyar hankali na kasuwar musayar kudin waje ta Ukraine a cikin sabon tsarin manufofin kuɗi.

2. Methodology & Data

The research employs a robust toolkit of time series econometrics to test three central hypotheses regarding the nature of exchange rate movements.

2.1 Time Series Analysis Framework

A multi-method approach is utilized:

• Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron tests to determine stationarity and identify stochastic trends.
• Autocorrelation Analysis (ACF/PACF): To inspect for serial correlation and guide ARMA model specification.
• Granger Causality Tests: To explore lead-lag relationships between the exchange rate and selected macroeconomic variables.
• Vector Autoregression (VAR) Model: Tsarin lokaci mai yawa don kama alaƙar tsakanin jerin lokaci da yawa.
• Impulse Response Functions (IRFs): An samo daga VAR model don gano tasirin girgizar daidaitaccen bambanci daga wani ma'auni akan farashin USD/UAH a tsawon lokaci.
• Rarrabuwar Trend da Na Zamani: Yin amfani da samfuran ARMA don raba jerin lokaci zuwa sassan trend, na zamani, da marasa tsari.

2.2 Data Description & Period

The primary data is the daily or monthly USD/UAH exchange rate from January 2014 to May 2020. The VAR model incorporates other macroeconomic indicators likely to influence the rate, such as (inference from context): Ukraine's international reserves, inflation differentials (Ukraine vs. US), key policy interest rates, and possibly commodity prices (e.g., steel, grain) crucial for Ukraine's exports. Data sources typically include the National Bank of Ukraine (NBU) and State Statistics Service of Ukraine.

3. Empirical Analysis & Results

3.1 Trend Analysis & Random Walk

Gwajen tushen rai (ADF, Phillips-Perron) sun kasa ƙin yarda da hasashen farko na tushen rai don jerin USD/UAH a matakan, yana nuna rashin tsayawa. Bayan bambancin farko, jerin ya zama tsayayye. Wannan alama ce ta gargajiya na tsarin tafiya bazuwar, wanda aka tsara shi kamar haka: $\Delta e_t = \mu + \epsilon_t$, inda $e_t$ shine ƙimar musayar log kuma $\epsilon_t$ farar amo ce. Ƙarshe shi ne cewa yanayin yana da babban ɓangaren bazuwar, yana sa sauye-sauye masu kaifi, waɗanda ba za a iya hasashe ba a kan lokaci ya zama sifa mai mahimmanci. Wannan ya yi daidai da raunin tsarin Hasashen Kasuwa Mai Inganci (EMH) don ƙima mai iyo, inda motsin farashin da ya gabata ba zai iya hasashen canje-canje na gaba ba.

3.2 Seasonality Detection

Despite the random walk, the decomposition analysis reveals a statistically significant seasonal pattern. The Hryvnia tends to depreciate in the first and second quarters (Q1, Q2) of the year and appreciate in the Q3 da Q4 (Q3, Q4). Wannan tsarin ya samo asali ne daga dalilai na zagayowar tattalin arzikin Ukraine, kamar:

• Q1/Q2 Rage darajar kudi: High energy import bills post-winter, agricultural pre-season imports, and potential tax-related currency demand.
• Q3/Q4 Appreciation: Influx of foreign currency from major harvest exports (grains, oilseeds) and remittances from migrant workers.

This finding contradicts the pure EMH but is consistent with market microstructure and periodic liquidity flows.

3.3 Sensitivity to External Shocks (VAR & Impulse Response)

The VAR model and Impulse Response Functions show that the USD/UAH rate reacts to shocks from specific macroeconomic indicators. For instance:

• A positive shock to global risk aversion (e.g., VIX index spike) likely causes UAH depreciation (capital flight).
• A negative shock to Ukraine's international reserves leads to depreciation (reduced market confidence).
• A positive shock to key export commodity prices leads to UAH appreciation (improved trade balance).

Crucially, the IRFs demonstrate that these reactions are short-term, statistically insignificant in the long run, and tend to fade out within a few periods. This indicates a degree of market efficiency where information is quickly absorbed, but more importantly, it suggests the market is stable—shocks do not cause persistent, destabilizing spirals.

4. Key Findings & Interpretation

5. Technical Details & Mathematical Framework

The core models are specified as follows:

1. ARMA(p,q) for Trend-Seasonal Analysis:
$e_t = c + \phi_1 e_{t-1} + ... + \phi_p e_{t-p} + \epsilon_t + \theta_1 \epsilon_{t-1} + ... + \theta_q \epsilon_{t-q} + S_t$
Where $S_t$ represents the seasonal component modeled via seasonal dummies or Fourier terms.

2. Vector Autoregression (VAR) Model:
$\mathbf{Y}_t = \mathbf{c} + \mathbf{\Phi}_1 \mathbf{Y}_{t-1} + ... + \mathbf{\Phi}_k \mathbf{Y}_{t-k} + \mathbf{u}_t$
Where $\mathbf{Y}_t$ is a vector containing the log USD/UAH rate and other endogenous variables (e.g., log reserves, interest rate differential). $\mathbf{u}_t$ is a vector of white noise innovations.

3. Impulse Response Function:
Derived from the moving average representation of the VAR: $\mathbf{Y}_t = \mathbf{\mu} + \sum_{i=0}^{\infty} \mathbf{\Psi}_i \mathbf{u}_{t-i}$.
The IRF traces the effect $\frac{\partial Y_{j,t+s}}{\partial u_{i,t}}$, which is the $(j,i)$ element of matrix $\mathbf{\Psi}_s$.

6. Results, Charts & Discussion

Figure 1: USD/UAH Exchange Rate (2014-2020). Shows the dramatic depreciation from ~8 UAH/USD in early 2014 to a peak near 28 in early 2015, followed by high volatility within a 23-28 range, with a notable low of 23.46 in Dec 2019.

Figure 2: First Differences (Returns) of USD/UAH. Visually confirms stationarity and clusters of volatility, indicative of heteroskedasticity (potential for GARCH modeling).

Figure 3: Autocorrelation Function (ACF) of Returns. Shows no significant autocorrelations at most lags, supporting the random walk hypothesis for the weak form.

Figure 4: Seasonal Decomposition Plot. Clearly illustrates the extracted seasonal component, highlighting the Q1/Q2 dip (depreciation) and Q3/Q4 rise (appreciation).

Figure 5-7: Impulse Response Functions. Each chart shows the response of the USD/UAH rate to a one-time shock from another variable in the VAR system. The typical pattern is a sharp initial jump (positive or negative) that decays to zero within 6-10 months, visually confirming the short-lived impact of shocks.

Tables: The 11 tables present detailed results of ADF tests (with different lags and specifications), Phillips-Perron tests, Granger causality test statistics, VAR lag order selection criteria (Likelihood Ratio, AIC, SC), and estimated coefficients for the ARMA and VAR models.

7. Analytical Framework: A Case Example

Scenario: A Ukrainian agricultural exporter wants to hedge currency risk for expected USD revenue from a grain sale in October 2024.

Application of Study's Findings:

1. Seasonality Check: October is in Q4, a historically strong period for the Hryvnia. The study's model would predict a seasonal tailwind for UAH appreciation.
2. Trend Uncertainty: The random walk component means the overall trend is unpredictable. The exporter cannot rely on a forecasted rate.
3. Risk Assessment: Using the VAR framework, the exporter's risk team would model potential shocks: e.g., a drop in global wheat prices (negative for UAH) vs. a rise in NBU reserves (positive for UAH). The IRFs suggest any shock's effect would be temporary.
4. Hedging Decision: Given the seasonal strength but high overall volatility, a prudent strategy might be to hedge 50-70% of the expected revenue via a forward contract with the bank, locking in a known rate, while leaving the remainder unhedged to benefit from potential Q4 appreciation.

This example shows how the paper's diagnostic results translate into a concrete risk management framework, even without point forecasts.

8. Future Applications & Research Directions

1. Integration of High-Frequency Data & Market Microstructure: Future research should incorporate order flow data from Ukrainian interbank platforms to distinguish between fundamental and liquidity-driven volatility, following the seminal work of Evans & Lyons (2002) on the microstructure of foreign exchange markets.

2. Machine Learning for Forecasting: Given the limitations of linear models in capturing complex nonlinearities in volatile markets, techniques like LSTM (Long Short-Term Memory) networks or Gradient Boosting could be applied, as seen in recent finance literature (e.g., Sezer et al., 2020), to explore if predictive accuracy can be improved beyond the random walk benchmark.

3. Modeling Volatility Clustering: The evident volatility clusters call for GARCH-family models (e.g., EGARCH, GJR-GARCH) to formally model and forecast the time-varying variance of USD/UAH returns, which is critical for Value-at-Risk (VaR) calculations for financial institutions.

4. Expanding the Factor Set: Incorporating geopolitical risk indices specific to the region, cryptocurrency flows (relevant for Ukraine), and more granular capital flow data could enhance the explanatory power of the multifactor model.

5. Policy Simulation: VAR model ya da aka kafa zai iya zama dakin gwaje-gwaje don yin kwaikwayon tasirin canjin kuɗi na manufofin NBU na zato (misali, canje-canje a cikin buƙatun ajiya, dokokin shiga tsakani na FX) kafin aiwatarwa.

9. Nassoshi

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(366a), 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. Evans, M. D., & Lyons, R. K. (2002). Order flow and exchange rate dynamics. Journal of Political Economy, 110(1), 170-180.
7. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181.
8. National Bank of Ukraine. (Various). Official statistics and reports. Retrieved from bank.gov.ua

10. Expert Analysis & Critical Review