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

Recent research has shown that a reliable vector autoregression (VAR) for forecasting and
structural analysis of macroeconomic data requires a large set of variables and modeling …

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

Modelling is fundamental to many fields of science and engineering. A model can be
thought of as a representation of possible data one could predict from a system. The …

Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic
volatility are not invariant to the way the variables are ordered due to the use of a lower …

Current practice largely follows restrictive approaches to market risk measurement, such as
historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit …

We provide a detailed summary of the large and vibrant emerging literature that deals with
the multivariate modeling of conditional volatility of financial time series within the framework …

We define deep kernel processes in which positive definite Gram matrices are progressively
transformed by nonlinear kernel functions and by sampling from (inverse) Wishart …

Risks are usually represented and measured by volatility–covolatility matrices. Wishart
processes are models for a dynamic analysis of multivariate risk and describe the evolution …

Gaussian Variational Approximations for High-dimensional State Space Models Page 1
Bayesian Analysis (2023) 18, Number 3, pp. 989–1016 Gaussian Variational Approximations …