Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

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Multivariate stochastic volatility (2009, methodology): “Liesenfeld and Richard (2004) proposed an alternative multi-move MCMC method based on EIS, which can be used to estimate SV models by maximum likelihood, as well as simulation smoothing.”
Multivariate Stochastic Volatility: A Review (2006, methodology): “While the AGIS technique has limited applicability, a closely related method, namely the efficient importance sampling (EIS) procedure, as proposed by Liesenfeld and Richard ( 2003 2006 ), is applicable to models with more flexible classes of distributions for the latent variables.”
Autoregressive Conditional Duration Models in Finance: A Survey of the Theoretical and Empirical Literature (2008, methodology): “We refer the interested reader to Liesenfeld and Richard (2006) for a classical and Bayesian analysis of SV models using efficient importance sampling.”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, methodology): “˜ 1 = G(1=2;1=(2B ˙)). 3 Statistical Inference There are a number of methods to estimate factor SV models such as quasi-maximum likelihood (e.g. Harvey et al.1994), simulated maximum likelihood (e.g. Liesenfeld and Richard 2006; Jungbacker and Koopman2006), and Bayesian MCMC simulation (e.g. Pitt an”