Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data

Posted: 16 Jun 2008

See all articles by George J. Jiang

George J. Jiang

Washington State University

Roel C. A. Oomen

Deutsche Bank AG (London); London School of Economics & Political Science (LSE) - Department of Statistics

Multiple version iconThere are 3 versions of this paper

Date Written: Winter 2007

Abstract

This article proposes a new 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.

Keywords: affine jump diffusion, generalized method of moments, high-frequency data, latent state variables, unbiased minimum-variance estimator

Suggested Citation

Jiang, George and Oomen, Roel C.A., Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data (Winter 2007). Journal of Financial Econometrics, Vol. 5, Issue 1, pp. 1-30, 2007. Available at SSRN: https://ssrn.com/abstract=1145506 or http://dx.doi.org/10.1093/jjfinec/nbl007

George Jiang (Contact Author)

Washington State University ( email )

Department of Finance and Management Science
Carson College of Business
Pullman, WA 99-4746164
United States
509-3354474 (Phone)

HOME PAGE: http://directory.business.wsu.edu/bio.html?username=george.jiang

Roel C.A. Oomen

Deutsche Bank AG (London) ( email )

Winchester House
1 Great Winchester Street
London, EC2N 2DB
United Kingdom

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

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