Estimation of a multivariate stochastic volatility density by kernel deconvolution

Bert Van Es and Peter Spreij

Journal of Multivariate Analysis, 2011, vol. 102, issue 3, 683-697

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.

Keywords: Stochastic; volatility; models; Multivariate; density; estimation; Kernel; estimator; Deconvolution; Mixing (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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