Detecting Long-range Power-law Correlations in Financial Time Series:A Case on Listed Companies of Taiwan Stock Market

Abstract

In time series analysis, there have been many statistic models widely used; some models could estimate long memory. A new idea for analyzing time series is Detrended Fluctuation Analysis (DFA), which was originally developed for finding long-rage power-law correlations in DNA sequences. We apply DFA to Taiwan stock market for three categories of data: TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index), the group indices aggregated from individual stock indices, and individual stock indices. The results show that long memory exists in most listed companies of Taiwan stock market for the cases when 5.0 ¹ a . However, the correlations detected from aggregated data series do not imply the correlation of original data series. Our findings are that the correlations detected from main index do not imply the same correlation of group indices and individual stock indices, but there are greater than half of group indices and individual stock indices following the same correlation with the main index.