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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 business sectors of the full time series for the SHSE and TWSE markets are compared with those of the fast mode.DG-Energy and RE-Utility are cluster pairs for the SHSE market, while RE-CI, EI-CI and DG-CI are those for the TWSE market. The abbreviations are as follows. RE: Real estate; CI: Chemical industry; SI: Steel industry; IG: Industrial goods; EI: Electronic industry; IT: Technology; BM: Basic materials; Serv: Service; DG: Daily consumer goods.
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pone.0139420.g003: The business sectors of the full time series for the SHSE and TWSE markets are compared with those of the fast mode.DG-Energy and RE-Utility are cluster pairs for the SHSE market, while RE-CI, EI-CI and DG-CI are those for the TWSE market. The abbreviations are as follows. RE: Real estate; CI: Chemical industry; SI: Steel industry; IG: Industrial goods; EI: Electronic industry; IT: Technology; BM: Basic materials; Serv: Service; DG: Daily consumer goods.

Mentions: Enlightened by the work in ref. [26], a methodology which combines the RMT theory with the planar maximally filtered graph and the alluvial diagram is introduced [54, 55]. The alluvial diagram is well applied to visualize the structural changes in network structures [55]. With this approach, we investigate the interaction structures of business sectors with the sector mode cross-correlation matrix, for the full time series, fast mode, medium mode and slow mode respectively. The alluvial diagrams of the business sectors for the SHSE and TWSE markets are shown in Fig 3. The height of a module is proportional to the number of its stocks. The module usually corresponds to a business sector, in which most of the stocks are running a same business. As shown in Fig 3, the structure of business sectors in the SHSE market for the fast mode is almost identical with that for the full time series. The identified cluster pairs are DG-Energy and RE-Utility. For the TWSE market, the behavior is similar.


Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

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

The business sectors of the full time series for the SHSE and TWSE markets are compared with those of the fast mode.DG-Energy and RE-Utility are cluster pairs for the SHSE market, while RE-CI, EI-CI and DG-CI are those for the TWSE market. The abbreviations are as follows. RE: Real estate; CI: Chemical industry; SI: Steel industry; IG: Industrial goods; EI: Electronic industry; IT: Technology; BM: Basic materials; Serv: Service; DG: Daily consumer goods.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139420.g003: The business sectors of the full time series for the SHSE and TWSE markets are compared with those of the fast mode.DG-Energy and RE-Utility are cluster pairs for the SHSE market, while RE-CI, EI-CI and DG-CI are those for the TWSE market. The abbreviations are as follows. RE: Real estate; CI: Chemical industry; SI: Steel industry; IG: Industrial goods; EI: Electronic industry; IT: Technology; BM: Basic materials; Serv: Service; DG: Daily consumer goods.
Mentions: Enlightened by the work in ref. [26], a methodology which combines the RMT theory with the planar maximally filtered graph and the alluvial diagram is introduced [54, 55]. The alluvial diagram is well applied to visualize the structural changes in network structures [55]. With this approach, we investigate the interaction structures of business sectors with the sector mode cross-correlation matrix, for the full time series, fast mode, medium mode and slow mode respectively. The alluvial diagrams of the business sectors for the SHSE and TWSE markets are shown in Fig 3. The height of a module is proportional to the number of its stocks. The module usually corresponds to a business sector, in which most of the stocks are running a same business. As shown in Fig 3, the structure of business sectors in the SHSE market for the fast mode is almost identical with that for the full time series. The identified cluster pairs are DG-Energy and RE-Utility. For the TWSE market, the behavior is similar.

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.