In this paper, we study the problem of the nonparametric estimation of the function m in a stochastic volatility model h_t = exp(X_t/2 Lambda )ξ_t, X_t = m(X_t - 1) + η_t, where ξ_t is a Gaussian white noise. We show that the model can be written as an autoregression with errors-in-variables. Then an adaptation of the deconvolution kernel estimator proposed by Fan and Truong [Annals of Statistics, 21, (1993) 1900] estimates the function m with the optimal rate, which depends on the distribution of the measurement error. The rates vary from powers of n to powers of ln(n) depending on the rate of decay near infinity of the characteristic function of this noise. The performance of the method are studied by some simulation experiments and some real data are also examined. Copyright 2004 Blackwell Publishing Ltd.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Did you know? You can import bibliographic info in various formats into you bibliographic tool, or just into your word processor. See under "publisher info" on each abstract page.