The third chapter introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. I analyze markets with and without learning and adaptation, and show that a model with learning and adaptation successfully replicates long-memories of return volatility, trading volume, and signs of market orders. I also argue what drives these long-memories, and conclude that evolution on trading strategy is crucial to understand those features.On the other hand, when an economy consists of more agents, who engage in experiential learning, the asymmetric volatility is more pronounced than the volatility clustering. Numerical examples would clarify the trade-off between the volatilityanbsp;...
|Title||:||Essays in Agent-based Computational Finance|
|Publisher||:||ProQuest - 2006|