Combining portfolio rules to improve prediction of global minimum variance portfolio weights
研究如何结合多种已有的预测规则，来更准确地预测全局最小方差投资组合的权重，对金融从业者优化资产配置有参考价值。
We consider the prediction of the global minimum variance portfolio (GMVP) weights based on realized covariance matrices computed from high-frequency intraday returns of risky assets. As the multivariate high-dimensional time series process of covariance matrices is rather complex and hard to estimate without substantial simplifications of the model structure, there exist various competing approaches for predicting the GMVP weights. Our major contribution is the development of a novel approach for combining several given GMVP prediction rules in order to determine a low dimensional time-varying vector of these rules’ proportions for the GMVP. We provide statistical results on realized rule proportions and suggest a feasible low-dimensional approach to forecast the proportions based on a set of pre-determined GMVP prediction rules. Our findings are illustrated in an empirical study where we forecast the GMVP weights based on 265 risky assets by combining various popular portfolio rules.