Exchange Market Pressure, Stock Prices, and Commodity Prices in Central and Eastern Europe

Table of Contents

1. Introduction

This paper investigates the interconnectedness of exchange, equity, and commodity markets in a set of Central and Eastern European (CEE) economies—namely the Czech Republic, Hungary, Poland, Ukraine, Bulgaria, and Romania. Despite the expectation of eventual Eurozone accession for many CEE EU members post-2004/2007 expansions, most, including major economies like Poland and Hungary, retain floating exchange rates and inflation-targeting regimes. This creates a complex environment where nominally independent currencies remain susceptible to spillovers from regional, Eurozone, and global financial shocks, particularly those transmitted via stock and commodity markets. The study's primary aim is to determine whether changes in domestic/foreign stock prices or global commodity prices exert pressure on these currencies to depreciate and to trace the direction and origin of these transmissions.

2. Methodology and Data

2.1 Exchange Market Pressure (EMP) Index Construction

The core of the empirical analysis is the construction of a monthly Exchange Market Pressure (EMP) index for each country from 1998 to 2017. The EMP index is a composite measure that captures speculative pressure on a currency, aggregating three key components:

1. Percentage change in the nominal exchange rate (local currency per foreign currency, e.g., EUR or USD).
2. Percentage change in international reserves (with a negative sign, as reserve losses indicate selling pressure).
3. Change in the interest rate differential (domestic vs. foreign, e.g., German rates).

The index is standardized to ensure comparability across countries and time. Periods of high positive EMP values are identified as potential currency crisis episodes.

2.2 Data Sources and Variables

The study utilizes monthly time-series data. Key variables include:

• EMP Index: Constructed as described above.
• Stock Returns: Domestic stock market indices (e.g., WIG for Poland, PX for Czech Republic) and foreign indices (e.g., Euro Stoxx 50, S&P 500).
• Commodity Prices: Changes in global indices for oil (e.g., Brent Crude) and a broad basket of commodities.
• Control variables may include measures of global risk aversion (e.g., VIX).

2.3 Econometric Framework: Vector Autoregression (VAR)

To examine dynamic linkages, the paper employs Vector Autoregressive (VAR) models. A VAR model treats all variables as endogenous and captures their interdependencies over time. The specific tools used are:

• Granger Causality Tests: To determine if past values of one variable (e.g., stock returns) contain statistically significant information for forecasting another (e.g., EMP). This indicates a directional predictive relationship.
• Impulse-Response Functions (IRFs): To trace the effect of a one-standard-deviation shock to one variable (e.g., a drop in oil prices) on the current and future values of another variable (e.g., EMP), illustrating the magnitude, direction, and persistence of spillovers.

3. Empirical Results and Analysis

3.1 EMP Trends and Currency Crises (1998-2017)

The constructed EMP indices reveal a significant spike in pressure across all studied CEE currencies during the 2008 Global Financial Crisis. A notable finding is that the intensity of central bank foreign exchange interventions (a component of EMP) generally decreased in the post-2008 period, suggesting a shift in policy or market structure.

3.2 Granger Causality Tests

The causality tests uncover heterogeneous transmission patterns:

• Czech Republic: Appears relatively insulated. Few significant causal links from foreign stock or commodity markets to domestic EMP are found.
• Hungary: Shows susceptibility to global spillovers, with causality running from world stock markets (e.g., S&P 500) to its EMP.
• Poland: Exposure is more intra-regional. Polish EMP is Granger-caused by stock market developments in other CEE countries.
• Ukraine: Exhibits a unique bidirectional causality between its domestic stock index and EMP. Furthermore, global commodity price changes Granger-cause Ukrainian EMP.

3.3 Impulse-Response Function Analysis

The IRFs provide a dynamic picture:

• A negative shock to global oil or commodity prices leads to a significant and persistent increase in EMP (pressure to depreciate) for Ukraine.
• For Hungary, a positive shock to Eurozone or US stock markets reduces EMP (eases pressure), aligning with the "risk-on" sentiment channel.
• Responses in Poland are more closely tied to shocks originating within the CEE region.

3.4 Country-Specific Findings

4. Discussion and Implications

4.1 Policy Implications for CEE Central Banks

The findings suggest that a "one-size-fits-all" policy approach is inadequate. Policymakers must tailor their surveillance and intervention frameworks based on their country's specific vulnerability profile:

• Hungary's National Bank should closely monitor global risk sentiment and capital flows.
• Poland's financial stability authorities need a strong focus on regional contagion channels.
• Ukraine's policymakers must incorporate commodity price forecasts into their exchange rate and reserve management strategies.

4.2 Limitations of the Study

The study acknowledges limitations: the use of monthly data may miss higher-frequency dynamics; the EMP index, while standard, has conceptual debates around its weighting; and the VAR framework establishes statistical linkages but does not explicitly identify the underlying economic channels (e.g., trade balance, portfolio flows).

5. Technical Details and Mathematical Framework

The core EMP index for country i at time t is constructed as follows:

$EMP_{i,t} = \frac{\Delta e_{i,t}}{\sigma_{\Delta e_i}} - \frac{\Delta r_{i,t}}{\sigma_{\Delta r_i}} + \frac{\Delta (i_{i,t} - i_{f,t})}{\sigma_{\Delta (i_i-i_f)}}$

Where:
$\Delta e_{i,t}$ = percentage change in exchange rate (LCU/FCU).
$\Delta r_{i,t}$ = percentage change in foreign reserves (negative sign).
$\Delta (i_{i,t} - i_{f,t})$ = change in interest rate differential.
$\sigma$ = standard deviation of the respective series over the sample, used for normalization.

The reduced-form VAR(p) model is specified as:
$Y_t = c + A_1 Y_{t-1} + A_2 Y_{t-2} + ... + A_p Y_{t-p} + u_t$
where $Y_t$ is a vector of endogenous variables (e.g., [EMP, Domestic Stock Returns, Oil Price Changes]), $c$ is a vector of constants, $A_j$ are coefficient matrices, and $u_t$ is a vector of white noise error terms.

6. Results and Chart Descriptions

Figure 1 (Hypothetical): Time Series of EMP Indices (1998-2017). A multi-panel chart showing the standardized EMP index for each of the six CEE countries. All series show pronounced peaks during 2008-2009. Ukraine's line displays the highest volatility and several major spikes outside of 2008, corresponding to its distinct political and economic crises. The Czech line appears the smoothest and least volatile.

Figure 2 (Hypothetical): Impulse-Response Functions for Ukraine. A panel of graphs. The key graph shows the response of Ukrainian EMP to a negative shock in World Oil Prices. The response is immediately positive (EMP increases), statistically significant for about 6-8 months, and then gradually decays to zero. Another graph shows the response of Ukrainian Stock Returns to a shock in Ukrainian EMP, confirming the bidirectional feedback loop.

7. Analytical Framework: Example Case Study

Scenario: A sharp 20% decline in global crude oil prices over a quarter.
Framework Application:

1. Direct Channel (Ukraine): Using the estimated IRF from the paper's model, we can quantify the expected increase in Ukraine's EMP index. This translates to a higher probability of hryvnia depreciation, reserve loss, or a need for interest rate hikes.
2. Indirect/Regional Channel (Poland): While Poland is less commodity-dependent, the oil shock may trigger a regional risk-off sentiment. The Granger-causality result suggests Polish EMP could be affected via spillovers from other CEE stock markets that react to the global growth fears induced by the oil price drop.
3. Portfolio Rebalancing Channel (Hungary): The oil shock may depress global equity markets (S&P 500). The established causality from global stocks to Hungarian EMP implies this could transmit pressure to the forint as international investors pull back from emerging markets.

This case study illustrates how the paper's empirical findings can be used to conduct stress-testing and scenario analysis for financial stability.

8. Future Applications and Research Directions

• High-Frequency Analysis: Replicating the study with daily or intraday data to capture faster spillovers, especially during crisis periods, similar to the high-frequency volatility spillover frameworks used in studies like Diebold & Yilmaz (2012).
• Network Analysis of Spillovers: Applying methodologies from Diebold & Yilmaz (2014) to model the CEE financial system as a network, quantifying each country's role as a transmitter or receiver of shocks.
• Integration with Macroeconomic Fundamentals: Extending the VAR to include variables like current account balances, credit growth, or fiscal indicators to move from correlation to a more structural understanding of channels.
• Machine Learning Enhancement: Using tools like LASSO-VAR or neural networks to handle a larger set of potential predictors and detect non-linear relationships that standard linear VARs might miss.
• Policy Simulation Tool: Developing a dashboard for central banks that inputs real-time data on global variables and outputs probabilistic forecasts of EMP based on the estimated models.

9. References

1. Hegerty, S. W. (2018). Exchange market pressure, stock prices, and commodity prices east of the Euro. Journal of Economics and Management, 31(1), 75-?.
2. Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.
3. Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134.
4. Kaminsky, G. L., & Reinhart, C. M. (1999). The twin crises: the causes of banking and balance-of-payments problems. American economic review, 89(3), 473-500.
5. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29.
6. International Monetary Fund (IMF). (2023). Global Financial Stability Report. Retrieved from https://www.imf.org.

10. Core Analyst Insight: A Four-Step Deconstruction

Core Insight: This paper delivers a crucial, often-overlooked truth: within the seemingly homogeneous "CEE bloc," financial vulnerability is not a monolith. The Czech Republic operates with a Swiss-like insulation, Hungary is a satellite of global capital flows, Poland is entangled in a regional web, and Ukraine is a classic commodity-driven emerging market with a volatile domestic feedback loop. Ignoring these fault lines is a recipe for mispriced risk.

Logical Flow: The author's approach is methodologically sound but conventional. Construct EMP indices → identify crisis periods → apply off-the-shelf VAR tools (Granger, IRFs). The power lies not in novel econometrics but in the careful application to an under-studied region. The logical leap from statistical result to economic interpretation (e.g., "global spillovers" vs. "regional contagion") is well-argued but, as they admit, stops short of pinning down the precise transmission mechanisms (carry trade unwinds? trade credit channels?).

Strengths & Flaws:
Strengths: The granular, country-by-country breakdown is the study's crown jewel. Moving beyond regional averages exposes critical idiosyncrasies. The focus on both equity AND commodity channels is comprehensive. The 1998-2017 sample robustly covers multiple crises.
Flaws: The monthly data frequency is a significant blind spot in today's algorithmic trading world; spillovers often happen in hours, not months. The EMP index, while standard, is a black box—its components (exchange rate, reserves, rates) can move in offsetting ways due to policy, masking true pressure. The study feels like a superb map of past terrain; its utility for forecasting the next crisis is limited without integrating forward-looking indicators or market sentiment data.

Actionable Insights:

1. For Investors: Toss out the "CEE ETF" mentality. Model Czech assets as low-beta to global finance, hedge Polish exposures against regional neighbors, and treat Ukraine as a leveraged bet on commodities with high political risk.
2. For Risk Managers: Build separate early-warning models for each country type identified. For Hungary, monitor the VIX and Fed policy. For Poland, create a regional financial conditions index. For Ukraine, anchor scenarios to oil price bands.
3. For Policymakers (CEE): The Czech National Bank's apparent success in decoupling is a case study to be reverse-engineered. Hungary and Poland must question if their monetary policy frameworks are sufficiently resilient to their dominant spillover channels. Ukraine's result is a stark warning to diversify its economy and build larger war chests.
4. For Researchers: This paper is the perfect foundation. The immediate next step is to re-run this analysis with daily data and incorporate network analysis tools (à la Diebold & Yilmaz) to move from bilateral causality to a systemic risk map of the entire CEE financial network.

In essence, Hegerty's work is less a forecasting tool and more a diagnostic framework—a vital first step in prescribing the right medicine for four very different patients.