Hidden temporal order unveiled in stock market volatility variance
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 pa...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2011-06-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/1.3598412 |
| _version_ | 1818516404900462592 |
|---|---|
| author | Y. Shapira D. Y. Kenett Ohad Raviv E. Ben-Jacob |
| author_facet | Y. Shapira D. Y. Kenett Ohad Raviv E. Ben-Jacob |
| author_sort | Y. Shapira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T00:41:42Z |
| format | Article |
| id | doaj.art-00296e4dc73249259b8150cf2a1d60b2 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-12-11T00:41:42Z |
| publishDate | 2011-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-00296e4dc73249259b8150cf2a1d60b22022-12-22T01:26:54ZengAIP Publishing LLCAIP Advances2158-32262011-06-0112022127022127-1410.1063/1.3598412025102ADVHidden temporal order unveiled in stock market volatility varianceY. Shapira0D. Y. Kenett1Ohad Raviv2E. Ben-Jacob3School of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978, IsraelSchool of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978, IsraelSchool of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978, IsraelSchool of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 69978, IsraelWhen 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.http://dx.doi.org/10.1063/1.3598412 |
| spellingShingle | Y. Shapira D. Y. Kenett Ohad Raviv E. Ben-Jacob Hidden temporal order unveiled in stock market volatility variance AIP Advances |
| title | Hidden temporal order unveiled in stock market volatility variance |
| title_full | Hidden temporal order unveiled in stock market volatility variance |
| title_fullStr | Hidden temporal order unveiled in stock market volatility variance |
| title_full_unstemmed | Hidden temporal order unveiled in stock market volatility variance |
| title_short | Hidden temporal order unveiled in stock market volatility variance |
| title_sort | hidden temporal order unveiled in stock market volatility variance |
| url | http://dx.doi.org/10.1063/1.3598412 |
| work_keys_str_mv | AT yshapira hiddentemporalorderunveiledinstockmarketvolatilityvariance AT dykenett hiddentemporalorderunveiledinstockmarketvolatilityvariance AT ohadraviv hiddentemporalorderunveiledinstockmarketvolatilityvariance AT ebenjacob hiddentemporalorderunveiledinstockmarketvolatilityvariance |