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

Torben G. Andersen and Bent E. Sørensen
Journal of Business & Economic Statistics
Vol. 14, No. 3 (Jul., 1996), pp. 328-352
DOI: 10.2307/1392446
Stable URL: http://www.jstor.org/stable/1392446
Page Count: 25
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GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
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Abstract

We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We 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 used 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 heteroscedasticity and correlation among the moments.

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