three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The …The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and … realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric …

. The model is estimated with sequential Monte Carlo methods that include a particle learning filter and a Rao …

. A state-dependent volatility spillover GARCH hedging strategy is developed to capture the regime switching global equity …

robust and efficient relative to alternative Markov Chain Monte Carlo schemes employed in such contexts. In addition it … model, namely SV-GARCH which attempts to bridge the gap between GARCH and stochastic volatility specifications. In nesting … the standard GARCH model as a special case, it has the attractive feature of inheriting the same unconditional properties …

the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for …This work deals with multivariate stochastic volatility models, which account for a time-varying variance … stochastic representations of the Wishart process and propose Markov-Switching Wishart processes to capture different regimes in …

DGP is a nonlinear state space model (SSM). We estimate the SSM using Bayesian tools grounded in a Rao …

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a … paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve … multivariate stochastic volatility in a flexible and realistic manner and they forecast comparably. In very high dimensional VARs …