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Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

Ouyang FY, Zheng B, Jiang XF - PLoS ONE (2015)

Bottom Line: However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent.The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days.More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Zhejiang University, Hangzhou 310027, China; School of Electronics and Information, Zhejiang University of Media and Communications, Hangzhou 310018, China; Collaborative Innovation Center of Advanced Microstructures, Nanjing 210093, China.

ABSTRACT
The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

No MeSH data available.


The return-volatility correlation function for the full time series and the fast mode.(a) The return-volatility correlation function for the HSI index in Hong Kong and the German DAX index, and that for the fast mode of each index. (b) The return-volatility correlation function averaged over the chosen 40 stocks for the NYSE market, and that of the fast mode.
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pone.0139420.g006: The return-volatility correlation function for the full time series and the fast mode.(a) The return-volatility correlation function for the HSI index in Hong Kong and the German DAX index, and that for the fast mode of each index. (b) The return-volatility correlation function averaged over the chosen 40 stocks for the NYSE market, and that of the fast mode.

Mentions: Firstly, we calculate the return-volatility correlation function for the German DAX index and the HSI index in Hong Kong, respectively. As shown in Fig 6(a), the return-volatility correlation function of the full time series for the two indices exhibit a leverage effect, while that of the fast mode just fluctuates around zero. This is a surprising result, since the equal-time correlation between the full time series and fast mode is very large, over 0.92 in general. For the Chinese indices, i.e., the average of the SHCI and SZCI indices, it is observed that before the year 2000, it exhibited a strong anti-leverage effect, while after 2000, it gradually changed to the leverage effect [53]. For comparison, we concentrate our attention on the period from 1990 to 2000. Again, the return-volatility correlation function for the fast mode fluctuates around zero. We have also analyzed the return-volatility correlation for individual stocks. We choose 40 stocks from the NYSE market, which show a strong leverage effect, and the results are displayed in Fig 6(b). The return-volatility correlation of the fast mode also fluctuates around zero. Thus we conclude that the fast mode dose not contribute to the leverage or anti-leverage effect.


Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

Ouyang FY, Zheng B, Jiang XF - PLoS ONE (2015)

The return-volatility correlation function for the full time series and the fast mode.(a) The return-volatility correlation function for the HSI index in Hong Kong and the German DAX index, and that for the fast mode of each index. (b) The return-volatility correlation function averaged over the chosen 40 stocks for the NYSE market, and that of the fast mode.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591268&req=5

pone.0139420.g006: The return-volatility correlation function for the full time series and the fast mode.(a) The return-volatility correlation function for the HSI index in Hong Kong and the German DAX index, and that for the fast mode of each index. (b) The return-volatility correlation function averaged over the chosen 40 stocks for the NYSE market, and that of the fast mode.
Mentions: Firstly, we calculate the return-volatility correlation function for the German DAX index and the HSI index in Hong Kong, respectively. As shown in Fig 6(a), the return-volatility correlation function of the full time series for the two indices exhibit a leverage effect, while that of the fast mode just fluctuates around zero. This is a surprising result, since the equal-time correlation between the full time series and fast mode is very large, over 0.92 in general. For the Chinese indices, i.e., the average of the SHCI and SZCI indices, it is observed that before the year 2000, it exhibited a strong anti-leverage effect, while after 2000, it gradually changed to the leverage effect [53]. For comparison, we concentrate our attention on the period from 1990 to 2000. Again, the return-volatility correlation function for the fast mode fluctuates around zero. We have also analyzed the return-volatility correlation for individual stocks. We choose 40 stocks from the NYSE market, which show a strong leverage effect, and the results are displayed in Fig 6(b). The return-volatility correlation of the fast mode also fluctuates around zero. Thus we conclude that the fast mode dose not contribute to the leverage or anti-leverage effect.

Bottom Line: However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent.The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days.More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Zhejiang University, Hangzhou 310027, China; School of Electronics and Information, Zhejiang University of Media and Communications, Hangzhou 310018, China; Collaborative Innovation Center of Advanced Microstructures, Nanjing 210093, China.

ABSTRACT
The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

No MeSH data available.