The literature on multivariate stochastic volatility (MSV) models has developed significantly
over the last few years. This paper reviews the substantial literature on specification …

Although stochastic volatility (SV) models have an intuitive appeal, their empirical
application has been limited mainly due to difficulties involved in their estimation. The main …

1.1 Introduction Research into time series models of changing variance and covariance,
which I will collectively call volatility models, has exploded in the last ten years. This activity …

Monetary policy and the private sector behaviour of the US economy are modelled as a time
varying structural vector autoregression, where the sources of time variation are both the …

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a
unified, practical likelihood-based framework for the analysis of stochastic volatility models …

This paper examines continuous‐time stochastic volatility models incorporating jumps in
returns and volatility. We develop a likelihood‐based estimation strategy and provide …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

This paper is concerned with simulation-based inference in generalized models of
stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of …

We discuss the development of dynamic factor models for multivariate financial time series,
and the incorporation of stochastic volatility components for latent factor processes …

This paper is concerned with the Bayesian estimation and comparison of flexible, high
dimensional multivariate time series models with time varying correlations. The model …