Abstract. The article considers local peculiarities of the world stock indices in 2007–first half 2012 and offers a comparative analysis of the indices. The research is based on the time series decomposition of the most liquid European, Asian, USA, and Brazil stock indices. The aim of the research was to localize and describe the crisis effects on index dynamics in time and scope by using wavelet decomposition techniques. This approach allows to identify clusters of stock indices and to study their common and individual features. The window transformation method is used for the investigation of index returns’ volatility dynamics. This method allows to investigate the nature and characteristics of the identified critical waves in the stock markets studied. The combined application of wavelet transform, neural networks and SSA is proposed for the prediction purposes. This approach is used for the return forecast of the German index DAX30.
Key words: economic crisis, stock index returns, the wavelet transform, neural networks, SSA
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