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An averaging framework for minimum-variance portfolios: Optimal rules for combining portfolio weights

Author

Listed:
  • Roland Füss

    (Swiss Finance Institute; University of St. Gallen - School of Finance)

  • Thorsten Glück

    (Wiesbaden Business School)

  • Christian Koeppel

    (University of St. Gallen)

  • Felix Miebs

    (University of Applied Sciences Cologne)

Abstract

We propose an averaging framework for combining minimum-variance strategies to either minimize the expected out-of-sample variance or maximize the expected out-of-sample Sharpe ratio. Our framework overcomes the problem of selecting the “best” strategy ex-ante by optimally averaging over portfolio weights. This averaging procedure has an intuitive economic interpretation because it resembles a fund-of-fund approach, where each minimum-variance strategy represents a single fund. In a range of simulations, for a set of well-established strategies, we show that optimally averaging over portfolio weights improves the out-ofsample variance and Sharpe ratio. We confirm the finding of our simulation study on empirical data.

Suggested Citation

  • Roland Füss & Thorsten Glück & Christian Koeppel & Felix Miebs, 2024. "An averaging framework for minimum-variance portfolios: Optimal rules for combining portfolio weights," Swiss Finance Institute Research Paper Series 24-10, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2410
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    More about this item

    Keywords

    Averaging; diversification; estimation error; portfolio optimization; shrinkage.;
    All these keywords.

    JEL classification:

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

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