Honggang Li

Emergent Volatility in Asset Markets with Heterogeneous Agents

This paper examines the emergence ofcomplex volatility in dynamic asset markets when there are heterogeneous agents. A discrete formulation is studied with two categories of market participants, fundamentalist traders who buy when the asset price is below the fundamental value and sell when it is above and noise traders who use moving average technical trading rules that can lead them to chase trends. Agents switch from one type of strategy to the other according to relative returns. A variety of outcomes are studied using numerical simulation, including variation of market price responsiveness to changes in excess demand, in switching behavior, and the introduction of noise. Bifurcation analysis of certain parameters is presented.

An Evolutionary Game Model of Financial Markets with Heterogeneous Players

Abstract Three types of market traders, including momentum traders, contrarian traders and fundamentalists, are introduced to an evolutionary game model as market players, and their payoff structures are given. Based on a discrete replicator equation, a dynamic system is defined, and then its evolutionarily stable states are presented, which correspond to different market price evolving processes, including the stationary price fluctuation around the fundamental value, the increasing (decreasing) price bubble and the stationary, fluctuating positive (negative) price bubble.

Expectations' Self-Fulfillment and Self-Destroying in Socio-Economic Systems

Expectations’ self-fulfillment and self-destroying co-exist in socio-economic systems, which makes the evolution of the systems seem to be mysterious to us. We regard these collective phenomenon arise from people’s ways to expect and act, namely, the expectation mode and action strategy, and propose a simple agent-based model in consideration of four direct combinations of general expectation modes and action strategies to detect the underlying mechanism. We find that the action strategy, or originally, the win-loss rule of the system, is indeed a crucial factor to determine whether the evolution of the systems would satisfy people’s expectations.

The Volatility of Return, Trading Volume and Amount in Different Scales

In this paper, we use different scales to examine the volatility of return, volume and trading amount by intra-daily high frequency data. Besides the conventional measures of the volatility that make use of the unobserved variance or standard deviation of its return, another two different types of return – absolute return, high-low return are introduced. Two models are considered. One is an ARMA model which is applied to stantionary series. The other is a MEM model called multiplicative error model jointed with expanded GARCH model. For Shanghai composite index, we find the relationship between these returns, and observe that both absolute return and high-low return are affected by asymmetric variables but with different extent. The estimation results of trading volume and amount show that their volatilities have significant cluster and persistence.

Stylized Facts in Different Dynamic Regimes of a Agent-Based Artificial Stock Market

Approaches of both theoretical analysis and computer simulation are used to study a stochastic multi-agent stock market model. Theoretical analysis provides the parameter settings for different dynamic regimes including fundamental equilibrium, non-fundamental equilibrium, periodicity and chaos. Agent-based computer simulations with those settings are performed to produce the price series. Statistical analysis of these data shows: markets of all regimes present stylized facts such as fat-tail, volatility clustering and long-term memory; the fundamental equilibrium regime has the most significant stylized facts, followed by periodicity and chaos, and non-fundamental equilibrium the least.