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 tradeoff 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.
Keywords
HeteroscedasticityGeneralized method of momentsMonte Carlo methodEconometricsWeightingStochastic volatilityAutoregressive modelIndirect InferenceMathematicsVolatility (finance)Computer scienceStatisticsEstimatorPanel data
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 14
- Issue
- 3
- Pages
- 328-352
- Citations
- 472
- Access
- Closed
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Cite This
Torben G. Andersen,
Bent E. Sørensen
(1996).
GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study.
Journal of Business and Economic Statistics
, 14
(3)
, 328-352.
https://doi.org/10.1080/07350015.1996.10524660
Identifiers
- DOI
- 10.1080/07350015.1996.10524660