论文标题
使用因子图形套索的最佳投资组合
Optimal Portfolio Using Factor Graphical Lasso
论文作者
论文摘要
图形模型是估计高维逆协方差（精度）矩阵的强大工具，该矩阵已应用于投资组合分配问题。这些模型做出的假设是精度矩阵的稀疏性。但是，当股票回报由共同因素驱动时，这种假设不存在。我们解决了此限制并开发一个框架图形开拉（FGL），该框架将图形模型与投资组合分配背景下的因子结构集成在一起，通过将精度矩阵分解为低级别和稀疏组件。我们的理论结果和模拟表明，FGL始终估计投资组合权重和风险敞口，并且FGL对重尾分配具有鲁棒性，这使我们的方法适合于财务应用。显示基于FGL的投资组合在标准普尔500分成分的经验应用中表现出优于几个杰出竞争对手，包括等级和索引投资组合。
Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for a portfolio allocation problem. The assumption made by these models is a sparsity of the precision matrix. However, when stock returns are driven by common factors, such assumption does not hold. We address this limitation and develop a framework, Factor Graphical Lasso (FGL), which integrates graphical models with the factor structure in the context of portfolio allocation by decomposing a precision matrix into low-rank and sparse components. Our theoretical results and simulations show that FGL consistently estimates the portfolio weights and risk exposure and also that FGL is robust to heavy-tailed distributions which makes our method suitable for financial applications. FGL-based portfolios are shown to exhibit superior performance over several prominent competitors including equal-weighted and Index portfolios in the empirical application for the S&P500 constituents.
