- Volume 9, Number 3 (2003), 451-465.
Nonparametric volatility density estimation
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.
Bernoulli, Volume 9, Number 3 (2003), 451-465.
First available in Project Euclid: 6 October 2003
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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