Estimation of continuous-time models with an application to equity volatility dynamics

• 成果类型: Article
• 署名作者: Bakshi, Gurdip; Ju, Nengjiu; Ou-Yang, Hui
• 署名单位: Hong Kong University of Science & Technology; University System of Maryland; University of Maryland College Park; Duke University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2005.09.005
• 发表日期: 2006
• 页码: 227-249
• 关键词: Continuous-time models; maximum-likelihood estimation; density approximation; equity volatility; market volatility dynamics

摘要

The treatment of this article renders closed-form density approximation feasible for univariate continuous-time models. Implementation methodology depends directly on the parametric-form of the drift and the diffusion of the primitive process and not on its transformation to a unit-variance process. Offering methodological convenience, the approximation method relies on numerically evaluating one-dimensional integrals and circumvents existing dependence on intractable multidimensional integrals. Density-based inferences can now be drawn for a broader set of models of equity volatility. Our empirical results provide insights on crucial outstanding issues related to the rank-ordering of continuous-time stochastic volatility models, the absence or presence of nonlinearities in the drift function, and the desirability of pursuing more flexible diffusion function specifications. (c) 2006 Elsevier B.V. All rights reserved.