Document Type
Article
Publication Date
4-1-2013
Abstract
This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discrete observations. A numerical solution to the stochastic differential equation is obtained by higher order Wagner-Platen approximation and it is used to derive the first two conditional moments. Monte Carlo simulation shows that the proposed method has good finite sample property for both normal and non-normal diffusions. In an application of estimating stochastic volatility models, we find evidence of closeness between the CEV model and the GARCH stochastic volatility model. This finding supports the discrete time GARCH modeling of market volatility.
Journal Title
Studies in Nonlinear Dynamics and Econometrics
Journal ISSN
1081-1826
Volume
17
Issue
2
First Page
179
Last Page
197
Digital Object Identifier (DOI)
10.1515/snde-2012-0026