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Estimation of Stochastic Volatility Models with Diagnostics (1995)
| Venue: | Journal of Econometrics |
| Citations: | 125 - 11 self |
BibTeX
@ARTICLE{Gallant95estimationof,
author = {A. Ronald Gallant and David Hsieh and George Tauchen},
title = {Estimation of Stochastic Volatility Models with Diagnostics},
journal = {Journal of Econometrics},
year = {1995},
volume = {81},
pages = {159--192}
}
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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 "Semiparametric ARCH" and "Nonlinear Nonparametric". With the first, the standard model is rejected, although some extensions are 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. Corresponding author: George Tauchen, Duke University, Department of Economics, Social Science Building, Box 90097, Durham NC 27708-0097 USA, phone 1-919-660-1812, FAX 1-919-684-8974, e-mail get@tauchen.econ.duke.edu. 0 1 Introduction The stochastic volatility model has been proposed as a descripti...
Keyphrases
stochastic volatility model score generator duke university several daily financial time series various extension nonlinear nonparametric adequate fit durham nc 27708-0097 usa george tauchen data analysis standard stochastic volatility model nonparametric specification efficient method social science building e-mail get tauchen standard model semiparametric arch
