Investor Sentiment and Stock Market Volatility: Evidence from the West African Regional Stock Exchange

Abstract

This study examines the relationship between investor sentiment and stock market volatility in the West African Economic and Monetary Union (WAEMU) using data from the West African Regional Stock Exchange (BRVM). Based on daily data spanning the period 2010–2022, five volatility models are estimated and compared: Generalized Autoregressive Conditional Heteroskedasticity [GARCH(1,1)], Exponential Generalized Autoregressive Conditional Heteroskedasticity [EGARCH(1,1)], Asymmetric Power ARCH [APARCH(1,1)], Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity [FIGARCH(1,d,1)] and the RiskMetrics model. The empirical results indicate a high degree of volatility persistence, pronounced asymmetric effects, and the presence of long-memory dynamics in BRVM stock returns. Among the competing specifications, the EGARCH model with an asymmetric Student’s t distribution provides the best overall performance in terms of model fit and forecasting accuracy. Furthermore, the inclusion of an investor sentiment index leads to an improvement in volatility forecasts, highlighting the relevance of behavioral factors in explaining market risk dynamics. These findings suggest that investor sentiment plays an important role in volatility dynamics in emerging African stock markets, while also underscoring the need for caution in interpreting sentiment-based measures due to potential methodological limitations.