Estimation of Continuous-Time Processes via the Empirical Characteristic Function

Posted: 18 Feb 2013

See all articles by George J. Jiang

George J. Jiang

Washington State University

John Knight

University of Western Ontario, Faculty of Social Science, Deparment of Economics (Deceased)

Date Written: February 17, 2002

Abstract

This article examines the class of continuous-time stochastic processes commonly known as afŽfine diffusions (AD’s) and afŽfine jump diffusions (AJD’s). By deriving the joint characteristic function, we are able to examine the statistical properties as well as develop an efficient estimation technique based on empirical characteristic functions (ECF’s) and a generalized method of moments (GMM) estimation procedure based on exact moment conditions. We demonstrate that our methods are particularly useful when the diffusions involve latent variables. Our approach is illustrated with a detailed examination of a continuous-time stochastic volatility (SV) model, along with an empirical application using S&P 500 index returns.

Suggested Citation

Jiang, George and Knight, John L., Estimation of Continuous-Time Processes via the Empirical Characteristic Function (February 17, 2002). Journal of Business & Economic Statistics, 20:2, 198-212 (2002), Available at SSRN: https://ssrn.com/abstract=2220053

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

John L. Knight

University of Western Ontario, Faculty of Social Science, Deparment of Economics (Deceased)

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