The impact of external shocks on volatility persistence and market efficiency of the foreign exchange rate regime: evidence from Malawi

The paper investigates whether external shocks act as shock absorbers or sources of shocks within Malawi’s flexible exchange rate regime, focusing on the persistence of exchange rate volatility and market efficiency of the MK/USD market. While conventional wisdom suggests flexible regimes insulate economies, recent evidence indicates small open economies remain vulnerable to global disturbances (e.g., financial crises, geopolitical events, pandemics). Malawi’s exchange rate history includes multiple regime changes and large devaluations (notably in 2012 and 2022), coinciding with global shocks (Russia–Ukraine conflict and Russian financial crisis), and the economy’s import dependence heightens sensitivity to external shocks. The study’s aims are to evaluate how exogenous shocks affect volatility persistence, whether volatility exhibits long memory, and whether the FX market is weak-form efficient. It formulates three hypotheses: (H1) external shocks have heterogeneous effects on FX volatility in a small open economy; (H2) external shocks significantly impact volatility persistence under a flexible regime; and (H3) external shocks significantly affect Malawi’s FX market efficiency.

The review contrasts fixed versus flexible exchange rate regimes via the Mundell–Fleming trilemma, noting flexible regimes offer policy autonomy but may not fully insulate against global shocks (Corsetti et al., Rey, etc.). Two mechanisms underpin asymmetric volatility responses: the leverage effect (Black; Christie) and the volatility feedback hypothesis (Nelson), both implying a negative volatility–return relation but from different causal directions. Empirical evidence shows asymmetric and spillover effects in FX markets, often with negative shocks dominating. Theories of exchange rate determination (PPP and Dornbusch’s overshooting model) provide expectations about mean reversion and short-run overshooting following shocks; persistence of deviations suggests influences beyond fundamentals. Prior Malawi-focused studies link RBM interventions to volatility with short memory in some contexts, and sectoral impacts differ across trading partners. The review highlights gaps: prior work is largely macro-focused, often studies single shocks, and predates COVID-19 and recent geopolitical events. The study proposes to assess multiple external shocks and their asymmetric effects on volatility and market efficiency in Malawi, formalizing hypotheses H1–H3.

Data comprise daily MK/USD interbank exchange rates from 20 June 2011 to 20 June 2022 (Reserve Bank of Malawi; compiled by Chunga and Yu, 2024). The analysis proceeds in two parts: (1) external shocks and volatility persistence; (2) external shocks and market efficiency. External shock peak dates are taken from IMF Global Financial Stability Reports and define rolling windows: Russia–Ukraine war (peak 2014-03-15), COVID-19 (2020-12-20), and Russian financial crisis (2022-11-15). Domestic control variables include inflation, current account balance (trade balance), and foreign exchange reserves. Pre-tests include Bai–Perron multiple structural breaks to detect shifts in returns and volatility, Engle’s ARCH LM test for conditional heteroskedasticity, and Ljung–Box tests for autocorrelation; first-differencing is used where necessary to address autocorrelation and stationarity in returns. Volatility models: symmetric GARCH(1,1) and asymmetric EGARCH(1,1) and TGARCH(1,1), augmented with external shock indicators and domestic controls. Volatility persistence is assessed via the sum of ARCH and GARCH parameters (stationarity requires the sum <1). Asymmetry is assessed in EGARCH (sign and significance of the leverage parameter; γ<0 indicates stronger impact of negative shocks) and in TGARCH via differing responses to positive vs negative shocks and an external shock interaction term. Structural breaks are included to capture shifts tied to external shocks. Market efficiency: mean equations include lagged returns, an interaction with external shocks, calendar anomalies (day-of-week, turn-of-month/year as applicable), and domestic controls to test weak-form predictability. A robustness check employs the Forward Rate Unbiasedness Hypothesis (FRUH), testing whether forward rates are an unbiased predictor of future spot rates (β0≈0 and β1≈1). Statistical inference is conducted across three windows (pre-2014m3, 2014m3–2017m10, post-2017m10) aligned with detected breaks and external shock periods.

- Structural breaks: Bai–Perron tests identify one break in returns (2014m3–2017m10; statistic 1296.8984, p<0.0001) coinciding with the Russia–Ukraine war window, and two breaks in volatility (first at 2015m2: statistic 6.0409, p=0.0488; second spanning 2015m8–2016m6: statistic 12.8831, p=0.0016) within the same broader window. - ARCH effects: ARCH LM tests up to 15 lags reject no-ARCH (all p-values 0.0000), confirming conditional heteroskedasticity. - Autocorrelation: Ljung–Box indicates significant autocorrelation in raw returns (Q=113.0413, p<0.0001), which is mitigated by first differencing; variance shows no significant autocorrelation. - Volatility persistence: GARCH-type models indicate pronounced long memory. Persistence metrics greatly exceed 1: GARCH(1,1) persistence ≈7.16; EGARCH(1,1) ≈15.49; TGARCH(1,1) bad/good news persistence ≈121.71/121.25. After including domestic controls (fully relaxed models), persistence remains high: GARCH ≈9.50; EGARCH ≈17.49; TGARCH bad/good ≈51.98/51.50. - Asymmetry and thresholds: EGARCH leverage parameter is negative and significant (e.g., −0.6614 in restricted; −0.5501 in relaxed), indicating negative shocks (bad news) raise volatility more than positive shocks. TGARCH shows larger impact of bad news vs good news (e.g., 121.7124 vs 121.2504; in relaxed 51.9819 vs 51.4992), with external shock interaction significant, corroborating asymmetric effects. - External versus domestic influences: The first break (linked to the Russia–Ukraine war) remains significant across models; the second break’s significance diminishes after adding domestic controls, suggesting domestic factors (inflation, reserves, trade balance) explain part of later volatility. Nevertheless, external shocks—especially negative ones—retain significant additive effects on volatility persistence even after controls. - Market efficiency (OLS with calendar effects): Lagged returns and external shock interactions are significant in two of the three windows, implying return predictability (violating weak-form efficiency). Calendar anomalies are significant, reinforcing predictability. - FRUH results: The forward rate coefficient far exceeds 1 during 2014m3–2017m10 (≈59.76, p<0.01) and post-2017m10 (≈74.74, p<0.01), rejecting unbiasedness; pre-2014m3 shows a large but statistically weaker estimate (≈26.72). The constant is not consistently zero outside the pre-shock window, further rejecting FRUH. These findings indicate the spot–forward 1:1 relationship fails and weak-form inefficiency prevails, worsening over time. - Overall: External shocks, particularly negative ones, heighten and prolong volatility (long memory), and degrade market efficiency (predictability in returns; FRUH failure).

Findings support H1–H3. External shocks exert heterogeneous, asymmetric effects on Malawi’s FX volatility, with negative shocks having larger and more persistent impacts (consistent with leverage and volatility feedback mechanisms). Persistence well above unity indicates long memory, implying that volatility clusters endure and interventions may have limited long-run effect. Even after controlling for domestic fundamentals (inflation, reserves, trade balance), external shocks—especially the Russia–Ukraine conflict—remain significant drivers of volatility, while some later volatility reflects domestic dynamics. For efficiency, significant return predictability and calendar effects, along with FRUH failure, indicate departures from weak-form efficiency that intensify in later periods. These outcomes suggest Malawi’s flexible regime does not fully insulate against global shocks; rather, shocks can transmit and amplify through micro-market channels, producing leverage and volatility feedback effects that sustain volatility and undermine efficiency. Policy-wise, bolstering buffers (FX reserves) and macroprudential soundness can help absorb external shocks, while acknowledging that central bank FX interventions may be short-lived in effectiveness in the face of persistent volatility.

The study shows that Malawi’s interbank FX market exhibits pronounced volatility persistence with strong asymmetry: negative external shocks (notably during the Russia–Ukraine war window) significantly increase and prolong volatility beyond domestic influences. After incorporating domestic controls, external shocks remain important, though some later breaks align with domestic factors. Market efficiency is compromised: returns are predictable (calendar anomalies present), and the FRUH 1:1 spot–forward relation fails, indicating weak-form inefficiency that worsens over time. Contributions include (i) integrating multiple global shocks to assess micro-transmission channels (volatility persistence and efficiency) in a small open economy; (ii) documenting structural breaks linked to external shocks; (iii) establishing that negative shocks have larger leverage/feedback effects; and (iv) corroborating that a flexible regime may function as a source/propagator of shocks rather than an absorber under certain conditions. Policy recommendations include strengthening foreign reserves (countercyclical buffers), maintaining macroprudential and fiscal discipline to reduce domestically driven volatility, and caution regarding legalizing informal FX markets which may exacerbate arbitrage and inefficiency during shock periods.