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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 of the full time series, the medium mode and that after removing the medium mode.(a) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the HSI index in Hong kong and the German DAX index. (b) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the NYSE market. The results are averaged over the chosen 40 stocks.
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pone.0139420.g007: The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode.(a) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the HSI index in Hong kong and the German DAX index. (b) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the NYSE market. The results are averaged over the chosen 40 stocks.

Mentions: After careful analyzing, we find that the medium mode contributes the most to the leverage and anti-leverage effects while the slow mode also does not. The average cycles are from fifteen days to over two hundred days. In Fig 7(a), the return-volatility correlation function is shown for the German DAX index and the HSI index in Hong Kong, and in Fig 7(b), the one averaged over the chosen 40 stocks for the NYSE market is displayed. In all these cases, the medium mode dominates the leverage effect. After removing the medium mode, the leverage effect is not detected. As shown in Fig 1, each frequency mode exhibits a quasi periodic behavior. Such a periodicity will be reflected in the return-volatility correlation function, especially for the medium and slow modes. Therefore, the curve for the medium mode in the figures is the average of the upper and lower envelopes of the original one.


Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

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

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

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

pone.0139420.g007: The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode.(a) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the HSI index in Hong kong and the German DAX index. (b) The return-volatility correlation function of the full time series, the medium mode and that after removing the medium mode for the NYSE market. The results are averaged over the chosen 40 stocks.
Mentions: After careful analyzing, we find that the medium mode contributes the most to the leverage and anti-leverage effects while the slow mode also does not. The average cycles are from fifteen days to over two hundred days. In Fig 7(a), the return-volatility correlation function is shown for the German DAX index and the HSI index in Hong Kong, and in Fig 7(b), the one averaged over the chosen 40 stocks for the NYSE market is displayed. In all these cases, the medium mode dominates the leverage effect. After removing the medium mode, the leverage effect is not detected. As shown in Fig 1, each frequency mode exhibits a quasi periodic behavior. Such a periodicity will be reflected in the return-volatility correlation function, especially for the medium and slow modes. Therefore, the curve for the medium mode in the figures is the average of the upper and lower envelopes of the original one.

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.