Dynamics of correlations in the stock market (original) (raw)
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A novel application of the correlation matrix formalism to study dynamics of the financial evolution is presented. This formalism allows to quantify the memory effects as well as some potential repeatable intradaily structures in the financial time-series. The present study is based on the high-frequency Deutsche Aktienindex (DAX) data over the time-period between November 1997 and December 1999 and demonstrates a power of the method. In this way two significant new aspects of the DAX evolution are identified: (i) the memory effects turn out to be sizably shorter than what the standard autocorrelation function analysis seems to indicate and (ii) there exist short term repeatable structures in fluctuations that are governed by a distinct dynamics. The former of these results may provide an argument in favour of the market efficiency while the later one may indicate origin of the difficulty in reaching a Gaussian limit, expected from the central limit theorem, in the distribution of returns on longer time-horizons.
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Universal and Non-Universal Properties of Cross-Correlations in Financial Time Series
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Temporal evolution of financial-market correlations
Physical Review E, 2011
We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.
International Journal of Business and Economics Research, 2021
The core focus of the study is to examine financial states using index effect on stock to stock correlations of developed, developing and emerging market. The three markets such as S&P 500, KOSPI 200 and DSE are declared as developed, developing and emerging market respectively. To study the similarity between stock price changes, we calculate the time series of the daily log return. Closing stock prices of the targeted markets have been used to measure the daily return of the stocks. To analyze the market mobility, Pearson correlation coefficient, partial correlation, and index effect on stock to stock correlation techniques have been applied. The study found that the companies of developed and emerging market are more strongly correlated than those of developing market during big crash. On the other hand, developing market shows less index effect on stock correlations during crisis. Moreover, insignificant index effect has been found in emerging market during calm state. No significant effect of DSE index on stock to stock correlations in the period of global financial crisis has been observed, implying that global financial crisis did not hit to the DSE in this period. Before the market crash, the interactions between stocks became low enough which corresponds to lower value of average correlation for all types of market. Finally, the change of correlation and partial correlation can be a good indicator to identify and predict the financial states of all the markets which will further helps the stakeholders to make proper economic decisions.