PDF《regulatory policies*》

The use of agent-based financial market models to test the effectiveness of regulatory policies* Frank H.

Westerhoff University of Bamberg Department of Economics Feldkirchenstrasse

21 D-96045 Bamberg Germany frank.westerhoff@uni-bamberg.de Abstract Models with heterogeneous interacting agents have proven to be quite successful in the past. For instance, such models are able to mimic the dynamics of financial markets quite well. The goal of our paper is to explore whether this approach may offer new insights into the working of certain regulatory policies such as transaction taxes, central bank interventions and trading halts. Although this strand of research is rather novel, we argue that agent-based models may be used as artificial laboratories to improve our understanding of how regulatory policy tools function. Keywords Financial markets;

technical and fundamental analysis;

transaction taxes;

central bank intervention;

trading halts. JEL classification G15;

G18. * I thank two anonymous referees, Blake LeBaron and Peter Winker for constructive comments and encouragement.

1 1 Introduction Financial markets repeatedly display spectacular bubbles and crashes. Well-known examples include the evolution of the prices for gold and silver in the early 1980s, the development of the DEM/USD exchange rate in the mid 1980s, the course of the Japanese stock market between

1985 and 1995, the world-wide collapse of the stock markets following the year

2000 and the turbulent swings of the euro since its launch in 1999. In addition, the volatility of financial markets is quite high. For instance, the DEM/USD exchange rate changed by about 0.5 percent every day between

1974 and 1998. Commodity and stock prices can be even more volatile. The price of crude oil varied on average by about 1.7 percent every day between

1986 and

2000 (see Shiller

2000 and Sornette

2003 for lively overviews). As a result, policy makers are periodically tempted to regulate financial markets using diverse mechanisms such as transaction taxes, central bank interventions and trading halts. However, it is not entirely clear how these tools may affect the dynamics of financial markets. Our goal is to address this important issue from an agent-based financial market perspective. According to Axelrod and Tesfatsion (2006), agent-based modeling is a method used to study systems (a) which are composed of interacting agents and (b) which exhibit emergent properties, i.e. properties arising from the interactions of agents, which cannot be deduced simply by aggregating the agents'

properties. Obviously, this approach is well suited for modeling many social and economic phenomena. Neither are all human beings alike nor may we truly understand features such as social norms, civil violence, residential segregation or fads and herding behavior by inspecting the action of an isolated representative agent. The collective volume of Tesfatsion and Judd (2006) contains a number of interesting social and economic applications of this theme.

2 Agent-based models used in finance have recently been reviewed by Hommes (2006) and LeBaron (2006). While LeBaron concentrates more strongly on large type models with hundreds of heterogeneous traders who may apply an evolving set of rather sophisticated trading strategies, Hommes focuses on small type models with only a few different types of traders. Without question, both approaches have their merits. Here, we follow the route of Hommes, restricting ourselves to fairly simple models. One advantage of this is that we are able to pin down some of the causalities acting inside these models. Our plan is to illustrate potential consequences of regulatory policies with the help of a specific agent-based model and to sketch some findings of related papers to illustrate what kind of new arguments we may obtain from such a research direction. For this reason, we develop a simple model in which market participants may use technical or fundamental trading strategies to determine their orders or they may abstain from the market. The decision in favor of one of these three strategies is repeated every trading period and is based on the strategies'