A jackknife-type estimator for portfolio revision |
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| Institution: | 1. University of St.Gallen, Swiss Institute of Banking and Finance (s/bf), Rosenbergstrasse 52, 9000 St. Gallen, Switzerland;2. EBS Business School, Department of Finance, Accounting and Real Estate, Gustav-Stresemann-Ring 3, 65189 Wiesbaden, Germany;3. Macquarie Capital (Europe) Limited, OpernTurm, Bockenheimer Landstrasse 2-4, 60306 Frankfurt, Germany;1. Norges Bank, Financial Stability Research, Bankplassen 2, P.O. Box 1179, Sentrum, Norway;2. BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway;1. School of Business, University of Alberta, Canada;2. Department of Economics, Hitotsubashi University, Japan;1. Department of Economics and Finance, Canisius College, 2001 Main Street, Buffalo, NY 14208, United States;2. School of Accounting and Finance, Faculty of Business, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong;1. Department of Accounting, Finance, and Economics, College of Business Administration, Winthrop University, 423 Thurmond Building, Rock Hill, SC 29733, United States;2. Department of Finance, Belk College of Business, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, United States |
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| Abstract: | This article proposes a novel approach to portfolio revision. The current literature on portfolio optimization uses a somewhat naïve approach, where portfolio weights are always completely revised after a predefined fixed period. However, one shortcoming of this procedure is that it ignores parameter uncertainty in the estimated portfolio weights, as well as the biasedness of the in-sample portfolio mean and variance as estimates of the expected portfolio return and out-of-sample variance. To rectify this problem, we propose a jackknife procedure to determine the optimal revision intensity, i.e. the percent of wealth that should be shifted to the new, in-sample optimal portfolio. We find that our approach leads to highly stable portfolio allocations over time, and can significantly reduce the turnover of several well established portfolio strategies. Moreover, the observed turnover reductions lead to statistically and economically significant performance gains in the presence of transaction costs. |
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| Keywords: | Portfolio optimization Optimal portfolio revision Out-of-sample performance evaluation Jackknife estimator Transaction costs |
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