Modelling and forecasting multivariate realized volatility
提出一种对已实现协方差矩阵时间序列建模的方法，用于预测多元风险，保证预测的正定性，并通过随机占优检验证明基于该模型的最优组合收益优于传统MGARCH模型，对任何风险厌恶投资者都更有利。
This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.