タイトル：Matrix exponential stochastic volatility with leverage and it’s extensions
A multivariate stochastic volatility model with the dynamic correlation and the leverage effect is described and estimated. The matrix exponential transformation is used to keep the time-varying covariance matrices positive definite. An efficient Bayesian estimation method using Markov chain Monte Carlo is proposed. Of particular interest is our approach for sampling the latent state variables from the conditional posterior distribution. A blocked multi-move Metropolis-Hastings sampling, in which the proposal density is derived from an approximating linear Gaussian state space model, is applied. The model and the techniques are illustrated with daily stock, bond, and exchange rate returns data of Japan. Additionally, recent developments with high frequency tick data are also presented.