GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
Torben Andersen and
Journal of Business & Economic Statistics, 1996, vol. 14, issue 3, 328-52
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.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (180) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Working Paper: GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study (1995)
Working Paper: EMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:14:y:1996:i:3:p:328-52
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano
More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().