Since the introduction of ARCH models close to 40 years ago, a wide range of models for
volatility estimation and prediction have been developed and integrated into asset …

I discuss recent research advances in Bayesian state-space modeling of multivariate time
series. A main focus is on the “decouple/recouple” concept that enables application of state …

A comprehensive comparison of the volatility predictive abilities of different classes of time-
varying volatility models is considered. The models include the exponential GARCH …

We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic
modeling of multivariate time series. The perspective is that of a decision-maker with a …

We propose to generalize the Wishart state-space model for realized covariance matrices of
asset returns in order to capture complex measurement error structures induced by modern …

Although stochastic volatility and GARCH (generalized autoregressive conditional
heteroscedasticity) models have successfully described the volatility dynamics of univariate …

This paper introduces a new factor structure suitable for modeling large realized covariance
matrices with full likelihood‐based estimation. Parametric and nonparametric versions are …

In many fields where the main goal is to produce sequential forecasts for decision making
problems, the good understanding of the contemporaneous relations among different series …

In this article, we establish a Cholesky-type multivariate stochastic volatility estimation
framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM) …

The single equicorrelation structure among several daily asset returns is promising and
attractive to reduce the number of parameters in multivariate stochastic volatility models …