Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1146058 | Journal of Multivariate Analysis | 2011 | 15 Pages |
Abstract
We consider a continuous time stochastic volatility model. The model contains a stationary volatility process. We aim to estimate the multivariate density of the finite-dimensional distributions of this process. We assume that we observe the process at discrete equidistant instants of time. The distance between two consecutive sampling times is assumed to tend to zero.A multivariate Fourier-type deconvolution kernel density estimator based on the logarithm of the squared processes is proposed to estimate the multivariate volatility density. An expansion of the bias and a bound on the variance are derived.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Bert Van Es, Peter Spreij,