A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test

Abstract

This paper presents an analysis of EU peripheral (so-called PIIGS) stock market indices and the S&P Europe 350 index (SPEURO), as a European benchmark market, over the pre-crisis (2004-2007) and crisis (2008-2011) periods. We computed a rolling-window wavelet correlation for the market returns and applied a non-linear Granger causality test to the wavelet decomposition coefficients of these stock market returns. Our results show that the correlation is stronger for the crisis than for the pre-crisis period. The stock market indices from Portugal, Italy and Spain were more interconnected among themselves during the crisis than with the SPEURO. The stock market from Portugal is the most sensitive and vulnerable PIIGS member, whereas the stock market from Greece tends to move away from the European benchmark market since the 2008 financial crisis till 2011. The non-linear causality test indicates that in the first three wavelet scales (intraweek, weekly and fortnightly) the number of uni-directional and bi-directional causalities is greater during the crisis than in the pre-crisis period, because of financial contagion. Furthermore, the causality analysis shows that the direction of the Granger cause-effect for the pre-crisis and crisis periods is not invariant in the considered timescales , and that the causality directions among the studied stock markets do not seem to have a preferential direction. These results are relevant to better understand the behaviour of vulnerable stock markets, especially for investors and policymakers.

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

“…The MODWT can handle samples of any size N, while the DWT restricts the sample size to a multiple of 2 j , where J is the level of the decomposition; Furthermore, while both the DWT and MODWT can be used for an analysis of variance based on wavelet and scaling coefficients, the MODWT wavelet variance estimator can be shown to be asymptotically more efficient than the same estimator based on DWT [17][18][19][20][21].…”

“…In addition, the wavelet coherency was used in order to analyze the correlation structure between co-movements at different time scales. Recently, Polanco-Martínez et al [20] analyzed EU peripheral (so-called PIIGS) stock market indices and the S&P Europe 350 index (SPEURO), as a European benchmark market, over the pre-crisis (2004)(2005)(2006)(2007) and crisis (2008-2011) periods. In the first step, they decomposed the daily log returns for the two time intervals (pre-crisis and 90 crisis periods) applying the MODWT with a Daubechies least asymmetric (LA) wavelet.…”

Dynamic links between the Nigerian equity market and those of selected regional and developed countries

“…Empirical studies that have examined stock market inter-linkages in other climes include but are not limited to Diebold and Yilmaz (2009) , Dasgupta (2014) , Maher et al. (2017) , Lean and Smyth (2014) , Ouattara (2017) , Sahar and Shah (2017) , Polanco-Martinez et al. (2018) , and Ahmad et al.…”

A wavelet approach towards examining dynamic association, causality and spillovers

“…The findings displayed the evidence of weak predictive power of oil price for forecasting gold price and strong predictive power of oil for volatility of gold market. Polanco-Martinez et al (2018) analyzed the dynamic association and causality of EU peripheral stock market indices and S&P Europe 350 (SPEURO) index during pre-crisis and crisis periods by Rolling-Window Wavelet Correlation (RWWC) and Diks-Pachenko nonlinear Granger causality test. Overall findings reported that stock indices of Portugal, Italy and Spain had strong interaction during crisis.…”

“…There is a literature which has tried to understand and measure the degree of interrelationship among homogeneous and heterogeneous assets (Genc et al, 2017;Mensi et al, 2017;Polanco-Martinez et al, 2018), understand contagion effects (Kalbaska and Gatkowski (2012), Kazemi and Sohrabji (2012)) and ascertaining causality (Reboredo et al, 2017;Shabaz et al, 2017). The existing literature may be segregated into two strands broadly.…”