Theory & Methods: Estimation of the Stochastic Volatility Model by the Empirical Characteristic Function Method

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Empirical Characteristic Function Estimation and Its Applications (2004, unknown): “The choice of p in the context of the ECF estimation is closely related to the well-known difficult choice of the block size in the moving block bootstrap method of Künsch (1989), as well as to the choice of bandwidth in the nonparametric setting.”
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, methodology): “Less efficient methods include GMM (Andersen and Sorensen, 1996; Melino and Turnbull, 1990), the quasi-maximum likelihood method (Harvey et al., 1994), and the method of the empirical characteristic function (Knight et al., 2002).”
Forecasting volatility in the New Zealand stock market (2002, methodology): “Such methods include quasi-maximum likelihood (QML) proposed by Ruiz (1994), simulated maximum likelihood (SML) by Danielsson (1994), GMM by Andersen and Sorensen (1996), Markov Chain Monte Carlo (MCMC) by Jacquier et al. (1994), and the empirical characteristic function (ECF) method by Knight et a”
Estimation methods for stochastic volatility models: a survey (2004, background): “Recently, Singleton (2001) and Knight et al. (2002) have proposed a new estimation procedure based on the empirical characteristic function (ECF).”