Estimation of Stochastic Volatility Models with Diagnostics

A. Gallant, David Hsieh and George Tauchen ()

No 95-36, Working Papers from Duke University, Department of Economics

Abstract: Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are Nonparametric ARCH and Nonlinear Nonparametric. With the first, the standard model is rejected, although some extensions are nearly accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient.

JEL-codes: G12 (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Published in JOURNAL OF ECONOMETRICS, 1997, pages 159-192

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Estimation of stochastic volatility models with diagnostics (1997)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:95-36

Access Statistics for this paper

More papers in Working Papers from Duke University, Department of Economics Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097.
Bibliographic data for series maintained by Department of Economics Webmaster ().