Estimation of Continuous-Time Processes via the Empirical Characteristic Function
针对仿射扩散和仿射跳扩散这类连续时间随机过程，推导联合特征函数，提出基于经验特征函数和广义矩估计的有效估计方法，无需离散化或模拟，尤其适用于含潜变量的模型，并用平方根随机波动模型和标普500指数回报数据验证。
This paper examines a particular class of continuous-time stochastic processes commonly known as afne diffusions (AD) and afne jump-diffusions (AJD). By deriving the joint characteristic function, we are able to examine the statistical properties as well as develop an efcient estimation technique based on empirical characteristic functions (ECF) and a GMM estimation procedure based on exact moment conditions. The estimators developed in this paper require neither discretization nor simulation. We demonstrate that our methods are in particular useful for the AD and AJD models with latent variables. We illustrate our approach with a detailed examination of the continuous-time square-root stochastic volatility (SV) model, along with an empirical application using S&P 500 index returns.