This study empirically tested the Fractal Market Hypothesis (FMH) in the Nigerian Stock Market (NSM) using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. The FMH suggests that financial markets exhibit fractal characteristics, with price movements influenced by heterogeneous agents and long-term memory, as opposed to the random walk assumption of the Efficient Market Hypothesis (EMH). Daily stock price data from 2015 to 2024 were analyzed to assess volatility dynamics, fractal behavior, and asymmetric effects in the NSM. The findings revealed significant volatility clustering and asymmetric effects in stock returns, consistent with fractal behaviour. The EGARCH model demonstrated that negative shocks increase volatility more than positive shocks of equal magnitude, highlighting the leverage effect in the NSM. This supports the FMH, suggesting that stock prices are influenced by historical patterns, thus challenging market efficiency in its strictest form. These results have implications for both market participants and regulators. Investors may exploit the fractal characteristics and volatility patterns to enhance risk management and trading strategies. Policymakers should consider these dynamics to improve market transparency and stability. Future research could explore the impact of macroeconomic shocks on these fractal properties and extend this analysis to other emerging markets.
Kenneth Uchenna Okafor (Thu,) studied this question.