Nonparametric volatility density estimation

Bert Es, van, P.J.C. Spreij, J.H. Zanten, van

    Research output: Contribution to journalArticleAcademicpeer-review

    17 Citations (Scopus)
    96 Downloads (Pure)


    We consider a continuous-time stochastic volatility model. The model contains a stationary volatility process, the density of which, at a fixed instant in time, we aim to estimate. We assume that we observe the process at discrete instants in time. The sampling times will be equidistant with vanishing distance. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared processes is proposed to estimate the volatility density. An expansion of the bias and a bound on the variance are derived.
    Original languageEnglish
    Pages (from-to)451-465
    Issue number3
    Publication statusPublished - 2003


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