Kurose, Yuta, and Yasuhiro Omori

The single equicorrelation structure among several daily asset returns is promising and at- tractive 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 oversimplication, the multiple- block equicorrelation structure is proposed for high dimensional nancial time series, where we assume common correlations within a group of asset returns, but allow different correla- tions for different groups. 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, we describe an efficient estimation method of Markov chain Monte Carlo simulation. Illustrative examples are given using simulated data, and empirical studies using U.S. daily stock returns data show that our proposed model outperforms other competing models in portfolio performances.