Indirect Estimation of the Parameters of Agent Based Models of Financial Markets
AbstractAgent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interac-tion of individuals provide a description of aggregate financial market time series. Al-though the outcomes of such simulations often exhibit similarities with real financial market time series, methods for explicit validation are required. This paper proposes validation using simulation based indirect estimation. It uses typical characteristic moments of financial market data to assess the similarity of simulation outcomes. Fur-thermore, the parameters of the agent based models can be estimated by maximizing this similarity. The paper presents details of this estimation approach and first results for the US–$/DM exchange rate.
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Bibliographic InfoPaper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp38.
Date of creation: Nov 2001
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Agent Based Models; Indirect Estimation; Validation;
Other versions of this item:
- Peter Winker & Manfred Gilli, 2002. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," Computing in Economics and Finance 2002 314, Society for Computational Economics.
- Peter Winker and Manfred Gilli, 2001. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," Computing in Economics and Finance 2001 59, Society for Computational Economics.
- Winmker, P. & Gilli, M., 2001. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," Papers 38, Manitoba - Department of Economics.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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