A multivariate stochastic volatility model with dynamic correlation and 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, using a blocked multi-move Metropolis-Hastings sampling, in which the
proposal density is derived from an approximating linear Gaussian state space model.
The proposed model is applied to the daily stock price index, the Japanese bond price
index, and the Yen/USD exchange rate returns data.
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