Comovement of Central European stock markets using wavelet coherence: Evidence from high-frequency data
Abstract
In this paper, we contribute to the literature on international stock market comovement and contagion. The novelty of our approach lies in usage of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock market returns in completely different way.
Major part of economic time series analysis is done in time or frequency domain separately. Wavelet analysis can combine these two funda- mental approaches, so we can work in time-frequency domain.
Using wavelet coherence, we have found very interesting dynamics of cross-correlations be- tween Central European and Western European stock markets. We analyze the high-frequency (5 minute) and low-frequency (daily) data of Czech (PX), Hungarian (BUX) and Polish (WIG) stock indices with a benchmark of German stock index (DAX) on the period of 2008-2009.
Our findings provide possibility of a new approach to financial risk modeling.