Estimation of Continuous-Time Processes via the Empirical Characteristic Function.

This article examines the class of continuous-time stochastic processes commonly known as affine diffusions (AD's) and affine 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.