Bernoulli
- Bernoulli
- Volume 9, Number 3 (2003), 451-465.
Nonparametric volatility density estimation
Bert Van Es, Peter Spreij, and Harry Van Zanten
Abstract
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.
Article information
Source
Bernoulli, Volume 9, Number 3 (2003), 451-465.
Dates
First available in Project Euclid: 6 October 2003
Permanent link to this document
https://projecteuclid.org/euclid.bj/1065444813
Digital Object Identifier
doi:10.3150/bj/1065444813
Mathematical Reviews number (MathSciNet)
MR1997492
Zentralblatt MATH identifier
1044.62037
Keywords
deconvolution density estimation kernel estimator mixing stochastic volatility models
Citation
Van Es, Bert; Spreij, Peter; Van Zanten, Harry. Nonparametric volatility density estimation. Bernoulli 9 (2003), no. 3, 451--465. doi:10.3150/bj/1065444813. https://projecteuclid.org/euclid.bj/1065444813
