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An Objective Function for Simulation Based Inference on Exchange Rate Data

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  • Manfred Gilli

    (University of Geneva)

  • Peter Winker

    ()
    (University of Giessen)

  • Vahidin Jeleskovic

    (University of Erfurt)

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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Bibliographic 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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Keywords: Indirect estimation; simulated based estimation; exchange rates;

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References

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Cited by:
  1. Grosche, Stephanie & Heckelei, Thomas, 2014. "Price dynamics and financialization effects in corn futures markets with heterogeneous traders," Discussion Papers, University of Bonn, Institute for Food and Resource Economics 172077, University of Bonn, Institute for Food and Resource Economics.
  2. Franke, Reiner & Westerhoff, Frank, 2011. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," BERG Working Paper Series 78, Bamberg University, Bamberg Economic Research Group.
  3. Grazzini, Jakob & Richiardi, Matteo, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," Department of Economics and Statistics Cognetti de Martiis. Working Papers, University of Turin 201335, University of Turin.
  4. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 4(8), pages 1-44.
  5. Jacob Grazzini & Matteo Richiardi & Lisa Sella, 2012. "Indirect estimation of agent-based models.An application to a simple diffusion model," LABORatorio R. Revelli Working Papers Series 118, LABORatorio R. Revelli, Centre for Employment Studies.
  6. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," EconomiX Working Papers 2012-17, University of Paris West - Nanterre la Défense, EconomiX.
  7. Manfred Gilli & Enrico Schumann, 2009. "Heuristic Optimisation in Financial Modelling," Working Papers, COMISEF 007, COMISEF.
  8. Franke, Reiner, 2009. "Applying the method of simulated moments to estimate a small agent-based asset pricing model," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(5), pages 804-815, December.
  9. Blake LeBaron & Peter Winker, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 141-148, June.
  10. Fischer, Thomas & Riedler, Jesper, 2013. "Prices, debt and market structure in an agent-based model of the financial market," ZEW Discussion Papers 12-045 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  11. Jakob Grazzini, 2011. "Consistent Estimation of Agent Based Models," LABORatorio R. Revelli Working Papers Series 110, LABORatorio R. Revelli, Centre for Employment Studies.
  12. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers, COMISEF 045, COMISEF.
  13. Frank H. Westerhoff, 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 195-227, June.
  14. Markus Demary, 2011. "Transaction taxes, greed and risk aversion in an agent-based financial market model," Journal of Economic Interaction and Coordination, Springer, vol. 6(1), pages 1-28, May.
  15. Annalisa Fabretti, 2013. "On the problem of calibrating an agent based model for financial markets," Journal of Economic Interaction and Coordination, Springer, vol. 8(2), pages 277-293, October.
  16. Gottfried Haber, 2008. "Monetary and Fiscal Policy Analysis With an Agent-Based Macroeconomic Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 276-295, June.

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