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Flexible shrinkage in portfolio selection

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  • Golosnoy, Vasyl
  • Okhrin, Yarema

Abstract

How to quantify estimation risk is important in portfolio selection. For this purpose we derive the flexible shrinkage estimator for the optimal portfolio weights, which allows dynamic adjustments of model structure. Our estimator is based on grouping the assets in order to capture non-homogeneity of estimation risk. The assets are assigned to groups using a clustering procedure with the number of groups determined from the data. The proposed flexible shrinkage approach exhibits sound and robust performance compared to the popular portfolio selection alternatives.

Suggested Citation

  • Golosnoy, Vasyl & Okhrin, Yarema, 2009. "Flexible shrinkage in portfolio selection," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 317-328, February.
    • Handle: RePEc:eee:dyncon:v:33:y:2009:i:2:p:317-328
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    References listed on IDEAS

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    Cited by:

    1. Vasyl Golosnoy & Nestor Parolya, 2017. "‘To have what they are having’: portfolio choice for mimicking mean–variance savers," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1645-1653, November.
    2. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    3. Mårten Gulliksson & Stepan Mazur, 2020. "An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 773-794, December.
    4. Fu, Yufen & Blazenko, George W., 2017. "Normative portfolio theory," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 240-251.
    5. Sukono & Dedi Rosadi & Di Asih I Maruddani & Riza Andrian Ibrahim & Muhamad Deni Johansyah, 2024. "Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model," Mathematics, MDPI, vol. 12(2), pages 1-22, January.
    6. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    7. Titi Purwandari & Riaman & Yuyun Hidayat & Sukono & Riza Andrian Ibrahim & Rizki Apriva Hidayana, 2023. "Selecting and Weighting Mechanisms in Stock Portfolio Design Based on Clustering Algorithm and Price Movement Analysis," Mathematics, MDPI, vol. 11(19), pages 1-22, October.
    8. Vasyl Golosnoy, 2018. "Sequential monitoring of portfolio betas," Statistical Papers, Springer, vol. 59(2), pages 663-684, June.
    9. Gillen, Benjamin J., 2014. "An empirical Bayesian approach to stein-optimal covariance matrix estimation," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 402-420.
    10. Vasyl Golosnoy & Benno Hildebrandt & Steffen Köhler, 2019. "Modeling and Forecasting Realized Portfolio Diversification Benefits," JRFM, MDPI, vol. 12(3), pages 1-16, July.
    11. Bajeux-Besnainou, Isabelle & Bandara, Wachindra & Bura, Efstathia, 2012. "A Krylov subspace approach to large portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 36(11), pages 1688-1699.

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