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Fund-of-funds construction by statistical multiple testing methods

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  • Michael Wolf
  • Dan Wunderli

Abstract

Fund-of-funds (FoF) managers face the task of selecting a (relatively) small number of hedge funds from a large universe of candidate funds. We analyse whether such a selection can be successfully achieved by looking at the track records of the available funds alone, using advanced statistical techniques. In particular, at a given point in time, we determine which funds significantly outperform a given benchmark while, crucially, accouting for the fact that a large number of funds are examined at the same time. This is achieved by employing so-called multiple testing methods. Then, the equal-weighted or the global minimum variance portfolio of the outperforming funds is held for one year, after which the selection process is repeated. When backtesting this strategy on two particular hedge fund universes, we find that the resulting FoF portfolios have attractive return properties compared to the 1/N portfolio (that is, simply equal-weighting all the available funds) but also when compared to two investable hedge fund indices.

Suggested Citation

  • Michael Wolf & Dan Wunderli, 2009. "Fund-of-funds construction by statistical multiple testing methods," IEW - Working Papers 445, Institute for Empirical Research in Economics - University of Zurich.
    • Handle: RePEc:zur:iewwpx:445
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    File URL: https://www.econ.uzh.ch/apps/workingpapers/wp/iewwp445.pdf
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    References listed on IDEAS

    as
    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Gregoriou, Greg N., 2006. "Funds of Hedge Funds," Elsevier Monographs, Elsevier, edition 1, number 9780750679848.
    6. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    7. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    8. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
    9. Kosowski, Robert & Naik, Narayan Y. & Teo, Melvyn, 2007. "Do hedge funds deliver alpha? A Bayesian and bootstrap analysis," Journal of Financial Economics, Elsevier, vol. 84(1), pages 229-264, April.
    10. Jobson, J D & Korkie, Bob M, 1981. "Performance Hypothesis Testing with the Sharpe and Treynor Measures," Journal of Finance, American Finance Association, vol. 36(4), pages 889-908, September.
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    Cited by:

    1. Enareta Kurtbegu & Juliana Caicedo-llano, 2014. "European equity fund managers: luck or skill?!," Economics Bulletin, AccessEcon, vol. 34(4), pages 2340-2350.
    2. Olivier Ledoit & Michael Wolf, 2019. "The power of (non-)linear shrinking: a review and guide to covariance matrix estimation," ECON - Working Papers 323, Department of Economics - University of Zurich, revised Feb 2020.
    3. McDowell, Shaun, 2018. "The benefits of international diversification with weight constraints: A cross-country examination," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 99-109.

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    More about this item

    Keywords

    Bootstrap; familywise error rate; fund-of-funds; performance evaluation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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