摘要翻译：
图形模型是估计高维逆协方差（精度）矩阵的有力工具，已应用于投资组合分配问题。这些模型的假设是精度矩阵的稀疏性。然而，当股票收益由共同因素驱动时，这样的假设就不成立了。我们解决了这一缺陷，并开发了一个框架--因子图形套索(FGL)，它通过将精确矩阵分解为低秩和稀疏的分量，将图形模型与投资组合分配中的因子结构结合起来。理论和仿真结果表明，FGL方法能够一致地估计投资组合的权重和风险敞口，并且对重尾分布具有鲁棒性，适合于金融应用。在S&P500成份股的实证应用中，基于FGL的投资组合显示出优于包括等权重投资组合和指数投资组合在内的几个突出竞争对手的性能。
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英文标题：
《Optimal Portfolio Using Factor Graphical Lasso》
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作者：
Tae-Hwy Lee and Ekaterina Seregina
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最新提交年份：
2021
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分类信息：

一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
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.
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