A Network View on Portfolio Risk
提出一种投资组合风险分解方法，利用网络视角量化资产间稀疏交互带来的网络风险，基于Dantzig型估计器和特征向量中心性降低高维估计误差，实证表明该方法在样本外表现优于现有方法。
Portfolio performance depends on interactions inside the network of assets. In high dimensions, these interactions are rare and sparse, therefore an inherent and indispensable portfolio risk, network risk, is difficult to quantify. Here a portfolio risk decomposition method is proposed that leads to analytical solutions that provide insights into the accounts for network and idiosyncratic risks. The technical platform is based on Dantzig-type estimator for covariance matrix and eigenvector centrality, which helps reduce estimation error in high-dimensional cases for portfolio optimization. Empirical results show that the network portfolio approach outperforms existing methods out-of-sample on a real dataset and demonstrate the solidity and reliability of our network portfolio and estimation methods from a practical perspective.