Estimating Latent Variables and Jump Diffusion Models Using High Frequency Data

39 Pages Posted: 19 Sep 2006 Last revised: 15 Feb 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

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Abstract

This paper 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 based on the observed state variables over extended horizons. With the estimates of the latent variables, we propose a 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, latent state variables, unbiased minimum-variance estimator, generalized method of moments, high frequency data

JEL Classification: C13, C22

Suggested Citation

Jiang, George and Oomen, Roel C.A., Estimating Latent Variables and Jump Diffusion Models Using High Frequency Data. Journal of Financial Econometrics, Vol. 5, No. 1, pp. 1-30, 2007. Available at SSRN: https://ssrn.com/abstract=929894

George Jiang

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 (Contact Author)

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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