The single equicorrelation structure among several daily asset returns is promising and attractive to reduce the number of parameters in multivariate stochastic volatility models. However, such an assumption may not be realistic as the number of assets may increase, for example, in the portfolio optimizations. As a solution to this oversimplification, the multiple-block equicorrelation structure is proposed for high dimensional financial time series, where common correlations within a group of asset returns are assumed, but different correlations for different groups are allowed. The realized volatilities and realized correlations are also jointly modelled to obtain stable and accurate estimates of parameters, latent variables and leverage effects. Using a state space representation, an efficient estimation method of Markov chain Monte Carlo simulation is described. Empirical studies using U.S. daily stock returns data show that the proposed model outperforms other competing models in portfolio performances.
