On the detection of extreme movements and persistent behaviour in Mediterranean stock markets: a wavelet-based approach

Abstract

We combine the global Hurst exponent and Morlet wavelet multi-resolution analysis (MRA) to investigate the dynamic behaviour of six selected stock markets in the Mediterranean region. Specifically, we employ the resonance coefficients and their power spectra to identify potential extreme movements and long-term dependence in stock returns. Using weekly data for the period 2005 to 2010, our results reveal that the wavelet MRA is able to reconstruct the effects of major extreme shocks on stock returns of studied markets, such as the Asian financial crisis, the 9/11 terrorist attacks and the 2007–2009 financial crisis. Moreover, the wavelet-based global Hurst exponent indicates the presence of long-term dependencies in stock returns of all the considered markets, except for France where the anti-persistent behaviour is detected. Overall, our findings are useful to assess the stock market efficiency and provide new insights into stock market dynamics over different time scales.