Market Efficiency and Volatility Dynamics in the Nigerian Stock Exchange: Evidence from the Pre- and Post-COVID-19 Lockdown Period
Abstract
The Efficient Market Hypothesis (EMH) posits that asset prices adjust rapidly to new information, leaving no scope for systematic excess returns. The COVID-19 pandemic represented an unprecedented global shock, generating substantial volatility across financial markets, including those in emerging African economies. This study examines the volatility dynamics and informational efficiency of the Nigerian Stock Exchange (NGX) All-Share Index before and after the COVID-19 lockdown. Using daily data from January 2018 to December 2022, the analysis applies GARCH (1,1) models under Gaussian, Student’s t, and Generalized Error Distribution assumptions to evaluate return dependence and volatility persistence across sub-periods. The empirical results confirm that returns are stationary in both pre- and post-lockdown phases. However, significant lagged return effects are observed across periods, indicating short-run return dependence inconsistent with strict weak-form efficiency. Volatility clustering is pronounced in the pre-lockdown period but weakens after the lockdown, with relatively rapid mean reversion of shocks in both phases. These findings suggest that the COVID-19 shock intensified volatility temporarily without fundamentally restructuring the informational dynamics of the Nigerian market. The study contributes to the emerging-market literature by distinguishing between volatility stabilisation and informational efficiency, demonstrating that reduced volatility persistence does not necessarily imply the elimination of return predictability. Strengthening market transparency and improving information dissemination remain essential for enhancing long-term market efficiency and resilience.
- The study examines weak-form efficiency and volatility dynamics of the Nigerian Exchange (NGX) using daily data (2018–2022).
- Significant lagged return effects are found in both pre- and post-lockdown periods, indicating short-run return dependence.
- Volatility clustering is pronounced before COVID-19 but weakens in the post-lockdown recovery phase.
- GARCH estimations reveal limited long-run volatility persistence and rapid mean reversion of shocks.
- Heavy-tailed error distributions (Student’s t and GED) provide superior model fit compared to Gaussian assumptions.
- COVID-19 intensified short-term volatility but did not fundamentally restructure the informational dynamics of the market.
- Reduced volatility persistence does not imply full restoration of weak-form market efficiency.
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Copyright (c) 2026 Kamaldeen Nageri, Fatimoh Toyin Abdulrahman
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