Bergische Universität Wuppertal
Fachbereich Mathematik und Naturwissenschaften
Angewandte Mathematik - Numerische Analysis

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Agent-Based Modelling in Financial Markets


International Internship Thesis


Supervision

Description

In this thesis we want to investigate the Kim-Markowitz microscopic multi-agent model of financial markets in order to explore relationships between the share of agents pursuing portfolio insurance strategies and the volatility of the market.
An agent based model is implemented using the language Netlogo, including the two types of investor agents 'rebalancers' and 'portfolio insurer'. While the rebalancer try to keep one half of their wealth in cash and invest the remaining half in stocks (thus stabilizing the market), the portfolio insurers follow the classical CPPI strategy proposed by Black and Jones, i.e. they try to keep the risky part of the assets in a constant proportion to the so-called 'cushion'. These insurers have a destabilizing effect on the market.
With the obtained simple agent based model we can simulate stock market crashes and discuss stabilizing effects of political restrictions of insurer's trading behaviour.

Keywords

agent based model, multi-agent dynamics, constant proportion portfolio insurance (CPPI), Kim-Markowitz model

References:

  1. S. Alfarano, T. Lux and F. Wagner, Estimation of agent-based models: the case of an asymmetric herding model, Comput. Econom. 26 (2005), 19-49.
  2. S. Alfarano, T. Lux and F. Wagner, Time variation of higher moments in financial markets with heterogeneous agents: an analytical approach, J. Econ. Dyn. Control 32 (2008), 101-136.
  3. P. Bak, M. Paczuski and M. Shubik, Price variations in a stock market with many agents, Physica A 246 (1997), 430-453.
  4. F. Black and R.C. Jones, Simplifying portfolio insurance, J. Portfolio Manag. 14 (1987), 48-51.
  5. S.H. Chen and C.H. Yeh, Evolving traders and the business school with genetic programming: a new architecture of the agent-based stock market, J. Econ. Dyn. Control 25 (2001), 363-393.
  6. A. De Martino and M. Marsili, Statistical mechanics of socio-economic systems with heterogeneous agents, J. Phys. A: Math. Gen. 39 (2006), R465-540.
  7. J. Duffy, Learning to speculate: experiments with artificial and real agents, J. Econ. Dyn. Control 25 (2001), 295-319.
  8. A. Gaunersdorfer, Endogenous fluctuations in a simple asset pricing model with heterogeneous agents, J. Econ. Dyn. Control 24 (2000), 799-831.
  9. D. Heymann, J.R.P. Perazzo and A. Schuschny, Learning and contagion effects in transitions between regimes: some schematic multi-agent models, 2001.
  10. G. Iori, A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions, J. Econ. Behav. Organ. 49 (2002), 269-285.
  11. T. Kaizoji, Speculative bubbles and crashes in stock markets: an interacting-agent model of speculative activity, Physica A 287 (2000), 493-506.
  12. G.W. Kim and H.M. Markowitz, Investment rules, margin and market volatility, J. Portfolio Manag. 16 (1989), 45-52.
  13. B. LeBaron, Empirical regularities from interacting long and short memory investors in an agent based stock market, IEEE Trans. Evol. Comput. 5 (2000), 442-455.
  14. B. LeBaron, Evolution and time horizons in an agent-based stock market, Macroecon. Dyn. 5 (2001), 225-254.
  15. T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of a financial market, Nature 397 (1999), 498-500.
  16. T. Lux and M. Marchesi, Volatility clustering in financial markets: a micro-simulation of interacting agents, Int. J. Theor. Appl. Finance 3 (2000), 67-702.
  17. T. Lux, The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions, J. Econ. Behav. Organ. 33 (1998), 143-165.
  18. A. Mandel, S. Fürst, W. Lass, F. Meissner and C. Jaeger, Lagom generiC an agent-based model of growing economies, European Climate Forum Working Paper 1/2009.
  19. R. Marimon, E. McGrattan and T.J. Sargent, Money as a medium of exchange in an economy with artificially intelligent agents, J. Econ. Dyn. Control 14 (1990), 329-373.
  20. E. Samanidou, E. Zschischang, D. Stauffer and T. Lux, Agent-based models of financial markets, Rep. Prog. Phys. 70 (2007), 409-450.
  21. M. Youssefmir and A. Huberman, Clustered volatility in multiagent dynamics, J. Econ. Behav. Organ. 32 (1997), 101-118.

Software:

  1. Ascape
  2. Flame
  3. Mason
  4. NetLogo
  5. Repast
  6. SeSAm
  7. Swarm

University of Wuppertal
Faculty of Mathematics and Natural Sciences
Department of Mathematics
Applied Mathematics & Numerical Analysis Group

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