Estimation of stochastic volatility models with diagnostics
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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. © 1997 Elsevier Science S.A.
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William Henry Glasson Professor of Economics
George Tauchen is the William Henry Glasson Professor of Economics and professor of finance at the Fuqua School of Business. He joined the Duke faculty in 1977 after receiving his Ph.D. from the University of Minnesota. He did his undergraduate work at the University of Wisconsin. Professor Tauchen is a fellow of the Econometric Society, the American Statistical Association, the Journal of Econometrics, and the Society for Financial Econometrics (SoFie). He is also the 2003 Duke University Sc