5,472 research outputs found
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 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. These two microscopic mechanisms and the methods for the
determination of the key parameters can be applied to other complex systems
with similar asymmetries.Comment: 17 pages, 6 figure
How volatilities nonlocal in time affect the price dynamics in complex financial systems
What is the dominating mechanism of the price dynamics in financial systems
is of great interest to scientists. The problem whether and how volatilities
affect the price movement draws much attention. Although many efforts have been
made, it remains challenging. Physicists usually apply the concepts and methods
in statistical physics, such as temporal correlation functions, to study
financial dynamics. However, the usual volatility-return correlation function,
which is local in time, typically fluctuates around zero. Here we construct
dynamic observables nonlocal in time to explore the volatility-return
correlation, based on the empirical data of hundreds of individual stocks and
25 stock market indices in different countries. Strikingly, the correlation is
discovered to be non-zero, with an amplitude of a few percent and a duration of
over two weeks. This result provides compelling evidence that past volatilities
nonlocal in time affect future returns. Further, we introduce an agent-based
model with a novel mechanism, that is, the asymmetric trading preference in
volatile and stable markets, to understand the microscopic origin of the
volatility-return correlation nonlocal in time.Comment: 16 pages, 7 figure
- …