Detecting and analyzing explosive bubbles and their relationship with volatility: evidence from Tunisia

Purpose This study aims to identify and analyze speculative bubbles in the Tunisian stock market from 2004 to 2023 and examine the evolution of return volatility during these periods. Design/methodology/approach The research uses the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests, alongside Monte Carlo and bootstrap simulations (Sieve-bootstrap and Wild-bootstrap), to detect speculative bubbles. The Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity model is used to analyze volatility regimes. Findings The study identifies multiple speculative bubbles with varying timing, duration and response to external events. The GSADF test proves more effective than the SADF test for detecting longer, more frequent bubbles. Despite methodological differences, strong correlations among bootstrap techniques improve bubble identification. Bubble periods align with a high-volatility regime (regime 2), emphasizing volatility’s role in bubble formation. Research limitations/implications This study enhances the understanding of speculative bubble formation in emerging markets, highlighting the importance of considering national financial market specifics in bubble analysis. Practical implications The findings offer valuable insights for investors, regulators and policymakers, helping inform decisions and improve financial regulation to foster market stability. Social implications By identifying speculative bubbles, the research helps mitigate economic uncertainty, protects savings and supports financial stability, aiding policymakers in curbing excessive speculation and promoting sustainable economic growth. Originality/value This research contributes to the understanding of speculative bubbles in the underexplored Tunisian stock market, using innovative methodologies for a comprehensive analysis of bubbles and volatility dynamics.