2011/05/27 by Yoash Shapira, Dror Y. Kenett, Ohad Raviv +1 · 2 citations
Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Financial Risk and Volatility Modeling #Time Series Analysis and Forecasting
paper · pdf · doi:10.1063/1.3598412
openalex publication_date 2011/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
When analyzed by standard statistical methods, the time series of the daily return of financial indices appear to behave as Markov random series with no apparent temporal order or memory. This empirical result seems to be counter intuitive since investor are influenced by both short and long term past market behaviors. Consequently much effort has been devoted to unveil hidden temporal order in the market dynamics. Here we show that temporal order is hidden in the series of the variance of the stocks volatility. First we show that the correlation between the variances of the daily returns and means of segments of these time series is very large and thus cannot be the output of random series, unless it has some temporal order in it. Next we show that while the temporal order does not show in the series of the daily return, rather in the variation of the corresponding volatility series. More specifically, we found that the behavior of the shuffled time series is equivalent to that of a random time series, while that of the original time series have large deviations from the expected random behavior, which is the result of temporal structure. We found the same generic behavior in 10 different stock markets from 7 different countries. We also present analysis of specially constructed sequences in order to better understand the origin of the observed temporal order in the market sequences. Each sequence was constructed from segments with equal number of elements taken from algebraic distributions of three different slopes.