Effects of Exponential Trends on Correlations of Stock Markets

Abstract

Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in time series. In this paper, DFA is employed to discuss the long-range correlations of stock market. The effects of exponential trends on correlations of Hang Seng Index (HSI) are investigated with emphasis. We find that the long-range correlations and the positions of the crossovers of lower order DFA appear to have no immunity to the additive exponential trends. Further, our analysis suggests that an increase in the DFA order increases the efficiency of eliminating on exponential trends. In addition, the empirical study shows that the correlations and crossovers are associated with DFA order and magnitude of exponential trends.

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…This is a crucial difference because the two regions indicate different behaviors concerning time correlation: short-term correlations are persistent, whereas long-term ones are anti-persistent. Similar effects can be found in other works, such as Varotsos et al 2003 [54], Lin et al 2014 [66], and Silva et al…”

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…This is a crucial difference, because the two regions indicate different behaviors concerning time correlation: short-term correlations are persistent, whereas long-term ones are anti-persistent. Similar effects can be found in other works, such as Varotsos et al, 2003 [57], Lin et al, 2014 [69], and Silva et al, 2004 [70]. To further explore this behavior,  was re-evaluated for short-term windows (n < 300) and long-term ones (n ≥ 300), respectively designated short  and high  .…”