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Jackknife Estimator for Tracking Error Variance of Optimal Portfolios

Listed author(s):
  • Gopal K. Basak

    ()

    (Indian Statistical Institute, Kolkata 700108, India)

  • Ravi Jagannathan

    ()

    (Northwestern University and NBER, Evanston, Illinois 60208)

  • Tongshu Ma

    ()

    (Binghamton University, Binghamton, New York 13902)

We develop a jackknife estimator for the conditional variance of a minimum tracking error variance portfolio constructed using estimated covariances. We empirically evaluate the performance of our estimator using an optimal portfolio of 200 stocks that has the lowest tracking error with respect to the S& P 500 benchmark when three years of daily return data are used for estimating covariances. We find that our jackknife estimator provides more precise estimates and suffers less from in-sample optimism when compared to conventional estimators.

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File URL: http://dx.doi.org/10.1287/mnsc.1090.1001
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Article provided by INFORMS in its journal Management Science.

Volume (Year): 55 (2009)
Issue (Month): 6 (June)
Pages: 990-1002

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Handle: RePEc:inm:ormnsc:v:55:y:2009:i:6:p:990-1002
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