Nonparametric volatility density estimation for discrete time models

B. Van Es, P. Spreij, J.H. van Zanten

Research output: Contribution to JournalArticleAcademicpeer-review


We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.
Original languageEnglish
Pages (from-to)237-251
JournalJournal of Nonparametric Statistics
Issue number2
Publication statusPublished - 2005

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