Optimal Portfolio Using Factor Graphical Lasso*

Tae Hwy Lee and Ekaterina Seregina

Journal of Financial Econometrics, 2024, vol. 22, issue 3, 670-695

Abstract: 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.

Keywords: approximate factor model; elliptical distributions; Graphical Lasso; high-dimensionality; portfolio optimization; Sharpe ratio (search for similar items in EconPapers)
JEL-codes: C13 C55 C58 G11 G17 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Optimal Portfolio Using Factor Graphical Lasso (2023)
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