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Portfolio selection with parsimonious higher comoments estimation

Author

Listed:
  • Lassance, Nathan

    (Université catholique de Louvain, LIDAM/LFIN, Belgium)

  • Vrins, Frédéric

    (Université catholique de Louvain, LIDAM/LFIN, Belgium)

Abstract

Large investment universes are usually fatal to portfolio strategies optimizing higher moments because of computational and estimation issues resulting from the number of parameters involved. In this paper, we introduce a parsimonious method to estimate higher moments that consists of projecting asset returns onto a small set of maximally independent factors found via independent component analysis (ICA). In contrast to principal component analysis (PCA), we show that ICA resolves the curse of dimensionality affecting the comoment tensors of asset returns. The method is easy to implement, computationally efficient, and makes portfolio strategies optimizing higher moments appealing in large investment universes. Considering the value-at-risk as a risk measure, an investment universe of up to 500 stocks and adjusting for transaction costs, we show that our ICA approach leads to superior out-of-sample risk-adjusted performance compared with PCA, equally weighted, and minimum-variance portfolios.

Suggested Citation

  • Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," LIDAM Reprints LFIN 2021005, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlr:2021005
    DOI: https://doi.org/10.1016/j.jbankfin.2021.106115
    Note: In: Journal of Banking & Finance, 2021, vol. 126(9), 106115
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    Citations

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    Cited by:

    1. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    2. Pier Francesco Procacci & Tomaso Aste, 2022. "Portfolio optimization with sparse multivariate modeling," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 445-465, October.
    3. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
    4. Lassance, Nathan & Vrins, Frédéric, 2023. "Portfolio selection: A target-distribution approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 302-314.
    5. Inés Jiménez & Andrés Mora-Valencia & Javier Perote, 2022. "Dynamic selection of Gram–Charlier expansions with risk targets: an application to cryptocurrencies," Risk Management, Palgrave Macmillan, vol. 24(1), pages 81-99, March.
    6. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2025. "The role of CDS spreads in explaining bond recovery rates," Journal of Banking & Finance, Elsevier, vol. 174(C).
    7. Eranda Çela & Stephan Hafner & Roland Mestel & Ulrich Pferschy, 2025. "Integrating multiple sources of ordinal information in portfolio optimization," Annals of Operations Research, Springer, vol. 346(3), pages 1967-1995, March.
    8. Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021. "Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-25, Septiembr.
    9. M. D. Braga & C. R. Nava & M. G. Zoia, 2023. "Kurtosis-based risk parity: methodology and portfolio effects," Quantitative Finance, Taylor & Francis Journals, vol. 23(3), pages 453-469, March.
    10. Lassance, Nathan, 2022. "Reconciling mean-variance portfolio theory with non-Gaussian returns," European Journal of Operational Research, Elsevier, vol. 297(2), pages 729-740.
    11. Conlon, Thomas & Cotter, John & Kynigakis, Iason, 2025. "Asset allocation with factor-based covariance matrices," European Journal of Operational Research, Elsevier, vol. 325(1), pages 189-203.
    12. Eranda c{C}ela & Stephan Hafner & Roland Mestel & Ulrich Pferschy, 2022. "Integrating multiple sources of ordinal information in portfolio optimization," Papers 2211.00420, arXiv.org, revised Jul 2023.
    13. Khashanah, Khaldoun & Simaan, Majeed & Simaan, Yusif, 2022. "Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process," International Review of Financial Analysis, Elsevier, vol. 81(C).
    14. Lu, Cheng & Ndiaye, Papa Momar & Simaan, Majeed, 2024. "Improved estimation of the correlation matrix using reinforcement learning and text-based networks," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    15. Wang, Yanfeng & Ke, Rui & Yang, Dong, 2024. "Modeling dynamic higher-order comoments for portfolio selection based on copula approach," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    16. Díaz, Antonio & Escribano, Ana & Esparcia, Carlos, 2024. "Sustainable risk preferences on asset allocation: a higher order optimal portfolio study," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).

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