28,538 research outputs found
Prospects for large-scale financial systems simulation
As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research
Agent-based simulation of a financial market
This paper introduces an agent-based artificial financial market in which
heterogeneous agents trade one single asset through a realistic trading
mechanism for price formation. Agents are initially endowed with a finite
amount of cash and a given finite portfolio of assets. There is no
money-creation process; the total available cash is conserved in time. In each
period, agents make random buy and sell decisions that are constrained by
available resources, subject to clustering, and dependent on the volatility of
previous periods. The model herein proposed is able to reproduce the
leptokurtic shape of the probability density of log price returns and the
clustering of volatility. Implemented using extreme programming and
object-oriented technology, the simulator is a flexible computational
experimental facility that can find applications in both academic and
industrial research projects.Comment: 11 pages, 3 EPS figures, LaTEX. To be published in Physica A
(Proceedings of the NATO Advanced Research Workshop on Application of Physics
in Economic Modelling, Prague 8-10 February 2001
Evolution and anti-evolution in a minimal stock market model
We present a novel microscopic stock market model consisting of a large
number of random agents modeling traders in a market. Each agent is
characterized by a set of parameters that serve to make iterated predictions of
two successive returns. The future price is determined according to the offer
and the demand of all agents. The system evolves by redistributing the capital
among the agents in each trading cycle. Without noise the dynamics of this
system is nearly regular and thereby fails to reproduce the stochastic return
fluctuations observed in real markets. However, when in each cycle a small
amount of noise is introduced we find the typical features of real financial
time series like fat-tails of the return distribution and large temporal
correlations in the volatility without significant correlations in the price
returns. Introducing the noise by an evolutionary process leads to different
scalings of the return distributions that depend on the definition of fitness.
Because our realistic model has only very few parameters, and the results
appear to be robust with respect to the noise level and the number of agents we
expect that our framework may serve as new paradigm for modeling self generated
return fluctuations in markets.Comment: 13 pages, 5 figure
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Diffusion and Aggregation in an Agent Based Model of Stock Market Fluctuations
We describe a new model to simulate the dynamic interactions between market
price and the decisions of two different kind of traders. They possess spatial
mobility allowing to group together to form coalitions. Each coalition follows
a strategy chosen from a proportional voting ``dominated'' by a leader's
decision. The interplay of both kind of agents gives rise to complex price
dynamics that is consistent with the main stylized facts of financial time
series.Comment: 17 pages, 8 figures (accepted for publication in Int. J. Mod. Phys.
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