Estimating latent variables and jump diffusion models using high-frequency data
Jiang, George J. and Oomen, Roel C. A.. (2007) Estimating latent variables and jump diffusion models using high-frequency data. Journal of Financial Econometrics, Volume 5 (Number 1). pp. 1-30. ISSN 1479-8409Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/jjfinec/nbl007
This article proposes anew approach to exploit the information in high-frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables. For this purpose, we construct unbiased estimators of the latent variables and their power functions on the basis of the observed state variables over extended horizons. With the estimates of the latent variables, we propose a generalized method of moments (GMM) procedure for the estimation of AJD models with the distinguishing feature that moments of both observed and latent state variables can be used without resorting to path simulation or discretization of the continuous-time process. Using high frequency return observations of the S&P 500 index, we implement our estimation approach to various continuous-time asset return models with stochastic volatility and random jumps.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HG Finance
H Social Sciences > HC Economic History and Conditions
|Divisions:||Faculty of Social Sciences > Warwick Business School|
|Journal or Publication Title:||Journal of Financial Econometrics|
|Publisher:||Oxford University Press|
|Number of Pages:||30|
|Page Range:||pp. 1-30|
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