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An Objective Function for Simulation Based Inference on Exchange Rate Data

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Author Info
Peter Winker (Department of Economics, University of Giessen)
Manfred Gilli (University of Geneva and Swiss Finance Institute)
Vahidin Jeleskovic (Department of Economics, University of Giessen)

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Abstract

The assessment of models of financial market behavior requires evaluation tools. When complexity hinders a direct estimation approach, e.g., for agent basedmicrosimulationmodels or complex 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 statistics of foreign exchange rate time series in previous research, an objective function is derived. This function takes into account stylized facts about the unconditional distribution of exchange rate returns and properties of the conditional distribution, in particular, autoregressive conditional heteroscedasticity and long memory. A bootstrap procedure is used to obtain an estimate of the variance-covariancematrix 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, a simple GARCH-model and a stochastic volatility model using the DM/US-$ exchange rate as a benchmark. It is also discussed how the results might be used for inference purposes.

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Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 07-01.

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Length: 20 pages
Date of creation: Feb 2007
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Handle: RePEc:chf:rpseri:rp0701

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Web page: http://www.SwissFinanceInstitute.ch
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Related research
Keywords: Indirect estimation; simulation based estimation; exchange rate returns;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
F31 - International Economics - - International Finance - - - Foreign Exchange

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    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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