Optimal Portfolio Using Factor Graphical Lasso

Tae Hwy Lee and Ekaterina Seregina

Papers from arXiv.org

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.

Date: 2020-11, Revised 2023-04
New Economics Papers: this item is included in nep-ore and nep-rmg
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Citations: View citations in EconPapers (1)

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Journal Article: Optimal Portfolio Using Factor Graphical Lasso* (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2011.00435

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