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Student Number 944209002
Author Ying-Fang Kao(°ª¼üªÚ)
Author's Email Address turkish0131@hotmail.com
Statistics This thesis had been viewed 2045 times. Download 599 times.
Department Economics
Year 2008
Semester 1
Degree Master
Type of Document Master's Thesis
Language English
Title Significance of Heterogeneity in Financial Markets: Empirical Studies from Simple Agent-Based Financial Models
Date of Defense 2008-11-09
Page Count 119
Keyword
  • Adaptive Belief System
  • Agent-Based Financial Model
  • Fundamentalist-Momentum
  • Abstract It has been long asked whether agent-based model can be a useful tool for forecasting, instead of just replicating or growing the stylized facts. In other words, in addition to provide a bottom-up mechanism to explain the stylized fact as an emergent outcome, agent based modeling has drawn
    further interests with regards to its estimation and prediction power. The recent research trend suggests that one can be cautiously optimistic about this possibility. This is particular so given the recent contribution by de Jong et al. (2006) and Manzan and Westerhoff (2007).
    Before the forecasting power of agent-based financial models are examined, more basic knowledge base should be carefully explore. Accordingly, this paper is devoted to an econometric study of the simple agent-based financial markets, which are based on the few-type designs. In particular, we follow the essential spirit of the adaptive belief system proposed by Brock and Hommes (1997) and Brock and Hommes (1998) in the context of fundamentalists-momentum trader and fundamentalists-momentum trader-contrarians formulation. The general question which con-
    cerns us is the significance of the number of types of agents, in particular, its contribution to replicating financial time series. A related issue, and a core issue, concerns the minimum number of types (clusters) required to replicate financial dynamics (Aoki, 2002). We are still uncertain about the general formulation of the issue because each type of financial agent can be designed
    in different ways. Hence, narrowing down the issue to the comparison between fundamentalist-chartist models and fundamentalist-chartist-contrarian models seems to be necessary at this stage. In this paper, we use financial data from 10 stock markets and 21 foreign exchange markets.
    Our econometric estimation of the simple agent-based financial markets can be related to Boswijk et al. (2007) and Amilon (2008). However, since the settings of the model are not exactly the same, our estimation method and technique are different from theirs. Because several parameters related to financial agents¡¦ behaviors, as emphasized in literature of heterogeneous agent financial
    model, are discrete variables and lead to a complex and nonlinear objective function (sum of squared error). Given this nature, the common numerical algorithms, which depend on the derivative of the objective function, is not available. Dorsey and Mayer (1995) suggest the use of
    genetic algorithms to deal with such situations and in this paper, we follow this recommendation.
    One of our main findings from the limited experiments is that given fundamentalists and chartists, additional contribution from contrarians is insignificant or not evident. While we do not have evidence in support of the 3-type model, the inclusion of the contrarians does destabilize our estimation in many ways. Besides, we are asking what kind of questions we can ask and how
    much we can learn from the agent-based financial markets with empirical data. Some specific questions asked in this paper may be considered as a special case of what can be more generally proposed. For example, we may consider the role of trading mechanism in the observed financial
    time series, the realistic time horizon perceived by the financial agents, and the influence of risks
    in agents'decision.
    Table of Content 1 Introduction and Motivation 1
    2 Literature Review 5
    2.1 ANT, IAH Models and Indirect Estimation . . . . . . . . . . . . . . . . . . . . . 5
    2.2 ABS and Direct Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
    3 The Agent-Based Financial Models 13
    3.1 Price Dynamics and Evolution of Market Fraction . . . . . . . . . . . . . . . . . 13
    3.2 Market Maker Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
    3.2.1 Heterogeneous Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
    3.2.2 Profit Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
    3.2.3 Risk Aversion and Risk Perception . . . . . . . . . . . . . . . . . . . . . . 17
    3.3 Walrasian Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
    4 The Econometric Model and Genetic Algorithm 21
    4.1 Walrasian Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
    4.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
    4.3 Definition and Initial Values of Parameters . . . . . . . . . . . . . . . . . . . . . 25
    4.4 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
    4.4.1 Using genetic algorithm as searching strategy . . . . . . . . . . . . . . . . 31
    4.4.2 Compare GA with Random . . . . . . . . . . . . . . . . . . . . . . . . . . 32
    5 Empirical Results 37
    5.1 Overall Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
    5.2 The Sensitivity of Parameters to Adding Contrarian . . . . . . . . . . . . . . . . 45
    5.3 Measure of Dispersion of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 47
    5.4 Distributions of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
    5.4.1 Fitness Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
    5.4.2 Intensity of Choice and Market Fraction . . . . . . . . . . . . . . . . . . . 54
    5.4.3 Memories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
    5.4.4 Risk Perception and Risk Aversion . . . . . . . . . . . . . . . . . . . . . . 60
    5.4.5 Behavior Rules of Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
    5.5 Extensive Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
    5.5.1 The Different Third Type . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
    5.5.2 Compare Dispersion of Parameters With Different Sample Period . . . . . 71
    5.5.3 Possible Recipe for Uneven Average Market Fraction . . . . . . . . . . . . 74
    6 Concluding Remarks 77
    6.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
    6.2 Main Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
    6.3 Research of Next Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
    A Tables of Estimates of Parameters 81
    A.1 Samples form 2005.1.1 to 2006.12.31 . . . . . . . . . . . . . . . . . . . . . . . . . 81
    A.2 Samples from 2001.1.1 to 2001.12.31 . . . . . . . . . . . . . . . . . . . . . . . . . 84
    B Distribution of Estimates from 2005.1.1-2006.12.31 87
    B.1 Sensitivity of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
    B.2 Heterogeneity of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
    C Extensive Issues 109
    D Pseudo Code and Other Estimating Models and Methods 111
    D.1 Pseudo Code under Walrasian scenario . . . . . . . . . . . . . . . . . . . . . . . . 111
    D.2 Estimation Method under Market Maker . . . . . . . . . . . . . . . . . . . . . . . 113
    D.3 Multiple Run and Bootstrap Method . . . . . . . . . . . . . . . . . . . . . . . . . 114
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    Advisor
  • Reuy Yau(«ÀºÍ)
  • Shu-Heng Chen(³¯¾ð¿Å)
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