Title: Maximum likelihood estimation of continuous time stochastic volatility models with partially observed GARCH
Authors: Niu, Wei-Fang
Institute of Statistics
Keywords: importance sampling;latent variable;simulated maximum likelihood;skewed normal distribution
Issue Date: 2013
Abstract: This paper proposes a method for the maximum likelihood estimation of continuous time stochastic volatility models. The key step is to introduce approximating GARCH processes that have higher frequencies of construction but are observed at lower frequencies. The latency of the volatility process is retained by augmenting data points between price observations. The convergence of the likelihood function can be obtained with mild regularity conditions. Such an approach reconciles discrete and continuous time models, and it can be implemented easily under the context of the simulated maximum likelihood. As an extension to the commonly used modified Brownian bridge sampler, we propose generating paths with skewed density to match the dynamics of the volatilities.
URI: http://hdl.handle.net/11536/22845
ISSN: 1081-1826
DOI: 10.1515/snde-2012-0017
Volume: 17
Issue: 4
Begin Page: 421
End Page: 438
Appears in Collections:Articles