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EMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study

  • Torben G. Andersen

    (Northwestern University)

  • Hyung-Jin Chung
  • Bent E. Sorensen

    (Brown University)

The authors examine alternative generalized method of moments procedures for estimation of a lognormal stochastic autoregressive volatility model by Monte Carlo methods. They document the existence of a trade-off between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is utilized to explore the impact of the weighting matrix for estimation, specification testing, and inference procedures. The results provide guidelines that help achieve desirable small sample properties in settings characterized by strong conditional heteroskedasticity and correlation among the moments.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1997 with number 6.

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Handle: RePEc:sce:scecf7:6
Contact details of provider: Postal: CEF97, Stanford University, Department of Economics, Stanford CA USA
Web page: http://bucky.stanford.edu/cef97/

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