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An Objective Function for Simulation Based Inference on Exchange Rate Data

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Author Info
Manfred Gilli (University of Geneva)
Peter Winker () (University of Giessen)
Vahidin Jeleskovic (University of Erfurt)

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Abstract

The assessment of models of financial market behaviour requires evaluation tools. When complexity hinders a direct estimation approach, e.g., for agent based microsimulation models or multifractal models, simulation based estimators might provide an alternative. In order to apply such techniques, an objective function is required, which should be based on robust statistics of the time series under consideration. Based on the identification of robust moments of foreign exchange rate time series in previous research, an objective function is derived. This function takes into account both stylized facts about the unconditional distribution of exchange rate returns and properties of the conditional distribution, in particular, autoregressive conditional heteroscedasticity and long memory. Results from a bootstrap procedure are used to obtain an estimate of the variance-covariance matrix of the different moments included in the objective function, which is used as a base for the weighting matrix. Finally, the properties of the objective function are analyzed for two different agent based models of the foreign exchange market using the DM/US-\$ as a benchmark

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Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 147.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:147

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Related research
Keywords: Indirect estimation; simulated based estimation; exchange rates;

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Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

References listed on IDEAS
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  1. Tesfatsion, Leigh S., 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Staff General Research Papers 12514, Iowa State University, Department of Economics. [Downloadable!]
    Other versions:
  2. M. Angeles Carnero, 2004. "Persistence and Kurtosis in GARCH and Stochastic Volatility Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 319-342. [Downloadable!] (restricted)
  3. Gilli, M. & Winker, P., 2003. "A global optimization heuristic for estimating agent based models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 299-312, March. [Downloadable!] (restricted)
  4. Kalaba, Robert & Tesfatsion, Leigh, 1996. "A multicriteria approach to model specification and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 21(2), pages 193-214, February. [Downloadable!] (restricted)
    Other versions:
  5. Lux, T. & M. Marchesi, . "Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market," Discussion Paper Serie B 438, University of Bonn, Germany, revised Jul 1998.
  6. Tesfatsion, Leigh S. & Judd, Kenneth L., 2003. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers 10368, Iowa State University, Department of Economics. [Downloadable!]
  7. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233 Elsevier. [Downloadable!] (restricted)
  8. Hommes, Cars & Huang, Hai & Wang, Duo, 2005. "A robust rational route to randomness in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(6), pages 1043-1072, June. [Downloadable!] (restricted)
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