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Agent-based model with multi-level herding for complex financial systems.

Chen JJ, Tan L, Zheng B - Sci Rep (2015)

Bottom Line: Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results.These properties are in agreement with the empirical ones.Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physics, Zhejiang University, Hangzhou 310027, China [2] Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

ABSTRACT
In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

No MeSH data available.


The average auto-correlation functions of volatilities for the NYSE and HKSE, and for the corresponding simulations.For clarity, the curves for the HKSE have been shifted down by a factor of 20.
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f2: The average auto-correlation functions of volatilities for the NYSE and HKSE, and for the corresponding simulations.For clarity, the curves for the HKSE have been shifted down by a factor of 20.

Mentions: From the calculation for the simulated returns, we obtain the sector structure and volatility clustering. For each stock i, the volatility clustering is characterized by the auto-correlation function of volatilities26, which is defined asHere , and represents the average over time t′. Thus, the auto-correlation function of volatilities averaged over all stocks is . As shown in Fig. 2, for both the NYSE and HKSE, A(t) for the simulations is in agreement with that for the empirical data.


Agent-based model with multi-level herding for complex financial systems.

Chen JJ, Tan L, Zheng B - Sci Rep (2015)

The average auto-correlation functions of volatilities for the NYSE and HKSE, and for the corresponding simulations.For clarity, the curves for the HKSE have been shifted down by a factor of 20.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4323661&req=5

f2: The average auto-correlation functions of volatilities for the NYSE and HKSE, and for the corresponding simulations.For clarity, the curves for the HKSE have been shifted down by a factor of 20.
Mentions: From the calculation for the simulated returns, we obtain the sector structure and volatility clustering. For each stock i, the volatility clustering is characterized by the auto-correlation function of volatilities26, which is defined asHere , and represents the average over time t′. Thus, the auto-correlation function of volatilities averaged over all stocks is . As shown in Fig. 2, for both the NYSE and HKSE, A(t) for the simulations is in agreement with that for the empirical data.

Bottom Line: Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results.These properties are in agreement with the empirical ones.Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physics, Zhejiang University, Hangzhou 310027, China [2] Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

ABSTRACT
In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

No MeSH data available.