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Optimal portfolio selection using a simple double-shrinkage selection rule

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  • Joo, Young C.
  • Park, Sung Y.

Abstract

In the field of risk management, it is of great importance to obtain an efficient portfolio when market participants invest in a variety of assets. In this study, we propose a simple double-shrinkage portfolio selection rule to improve the out-of-sample performance of the portfolio. The double-shrinkage portfolio is obtained by a convex combination between highly structured covariance matrices and sample covariance matrix. Using various real datasets we show that the proposed portfolio strategy is found to be comparatively stable and yields higher values of Sharpe-ratio and lower values of conditional value at risk. Thus, the double-shrinkage selection rule improves the performances of the portfolios significantly.

Suggested Citation

  • Joo, Young C. & Park, Sung Y., 2021. "Optimal portfolio selection using a simple double-shrinkage selection rule," Finance Research Letters, Elsevier, vol. 43(C).
    • Handle: RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321001008
    DOI: 10.1016/j.frl.2021.102019
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    Cited by:

    1. Wang, Xuanci & Zhang, Bin, 2024. "Target selection in shrinkage estimation of covariance matrix: A structural similarity approach," Statistics & Probability Letters, Elsevier, vol. 208(C).
    2. Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024. "Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem," Annals of Operations Research, Springer, vol. 334(1), pages 133-155, March.

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    More about this item

    Keywords

    Portfolio selection; Shrinkage estimation; Sparse covariance matrix; LASSO;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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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