Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 490, Issue -, Pages 1211-1227Publisher
ELSEVIER
DOI: 10.1016/j.physa.2017.08.065
Keywords
Non-stationary time series; Nonlinear causality test; MODWT; PIIGS; Rolling-window wavelet correlation
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Funding
- Basque Government post-doctoral fellowship
- UPV/EHU Econometrics Research Group (Basque Government Department of Education grant) [IT-642-13]
- Spanish Ministry of Economy, Industry and Competitiveness [TM2016-74931]
- Ramon y Cajal Research Fellowship of the Ministry of Economy and Competitiveness of Spain [RYC-2013-13628, RYC-2012-12167]
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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 time-scales, 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. (C) 2017 Elsevier B.V. All rights reserved.
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