Agent-based model with asymmetric trading and herding for complex financial systems.

BACKGROUND: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage...

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Main Authors: Jun-Jie Chen, Bo Zheng, Lei Tan
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3835857?pdf=render
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author Jun-Jie Chen
Bo Zheng
Lei Tan
author_facet Jun-Jie Chen
Bo Zheng
Lei Tan
author_sort Jun-Jie Chen
collection DOAJ
description BACKGROUND: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results. METHODS: To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors' asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data. RESULTS: With the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities. CONCLUSIONS: We reveal that for the leverage and anti-leverage effects, both the investors' asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors' trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.
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spelling doaj.art-af9c9c4433a14c7480df858fb5c5f6402022-12-22T01:08:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01811e7953110.1371/journal.pone.0079531Agent-based model with asymmetric trading and herding for complex financial systems.Jun-Jie ChenBo ZhengLei TanBACKGROUND: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results. METHODS: To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors' asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data. RESULTS: With the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities. CONCLUSIONS: We reveal that for the leverage and anti-leverage effects, both the investors' asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors' trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.http://europepmc.org/articles/PMC3835857?pdf=render
spellingShingle Jun-Jie Chen
Bo Zheng
Lei Tan
Agent-based model with asymmetric trading and herding for complex financial systems.
PLoS ONE
title Agent-based model with asymmetric trading and herding for complex financial systems.
title_full Agent-based model with asymmetric trading and herding for complex financial systems.
title_fullStr Agent-based model with asymmetric trading and herding for complex financial systems.
title_full_unstemmed Agent-based model with asymmetric trading and herding for complex financial systems.
title_short Agent-based model with asymmetric trading and herding for complex financial systems.
title_sort agent based model with asymmetric trading and herding for complex financial systems
url http://europepmc.org/articles/PMC3835857?pdf=render
work_keys_str_mv AT junjiechen agentbasedmodelwithasymmetrictradingandherdingforcomplexfinancialsystems
AT bozheng agentbasedmodelwithasymmetrictradingandherdingforcomplexfinancialsystems
AT leitan agentbasedmodelwithasymmetrictradingandherdingforcomplexfinancialsystems