Estimating Latent Variables and Jump Diffusion Models Using High Frequency Data

George J. Jiang, Roel C. A. Oomen
<span title="">2006</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">Social Science Research Network</a> </i> &nbsp;
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
more &raquo; ... 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, highfrequency data, latent state variables, unbiased minimum-variance estimator Jiang is from the Eller College of Management, University of Arizona, Tucson AZ 85721-0108, USA. Oomen is from the
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.2139/ssrn.925183</a> <a target="_blank" rel="external noopener" href="">fatcat:52w7mzxzqbflxp44lsct5bsgni</a> </span>
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