Title: Multivariate stochastic volatility models with realized volatility and pairwise realized correlation
Authors: Yuta Yamauchi - University of Tokyo (Japan) [presenting]
Yasuhiro Omori - University of Tokyo (Japan)
Abstract: Although stochastic volatility and GARCH models have been successful to describe the volatility dynamics of univariate asset returns, their natural extension to the multivariate models with dynamic correlations has been difficult due to several major problems. We consider dynamic latent correlation variables in addition to latent volatility variables and estimate model parameters using Markov chain Monte Carlo simulations, where we sample latent correlation variables one at a time given others so that we keep the covariance matrices positive definite. Our contributions are: (1) we obtain the stable parameter estimates for dynamic correlation models using the realized measures, (2) we make full use of intraday information by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, and (5) propose the flexible correlation structure model (e.g. such as setting some correlations to be identically zeros if necessary). Our proposed models are applied to daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations, and are shown to outperform the existing models with regard to portfolio optimizations.