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Optimal Portfolio Using Factor Graphical Lasso

Author

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  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

  • Ekaterina Seregina

    (Colby College)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Tae-Hwy Lee & Ekaterina Seregina, 2023. "Optimal Portfolio Using Factor Graphical Lasso," Working Papers 202302, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202302
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    References listed on IDEAS

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    Cited by:

    1. Tae-Hwy Lee & Ekaterina Seregina, 2022. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Papers 2209.01697, arXiv.org, revised Sep 2023.
    2. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.
    3. Yuanrong Wang & Tomaso Aste, 2022. "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers 2203.03991, arXiv.org.
    4. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
    5. Yuanrong Wang & Tomaso Aste, 2021. "Dynamic Portfolio Optimization with Inverse Covariance Clustering," Papers 2112.15499, arXiv.org, revised Jan 2022.

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    More about this item

    Keywords

    High-dimensionality; Portfolio optimization; Graphical Lasso; Approximate Factor Model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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