A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1 / Gopal K. Basak, Ravi Jagannathan, Tongshu Ma.

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Basak, Gopal K

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w10447.Publication details: Cambridge, Mass. National Bureau of Economic Research 2004.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: 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&P500 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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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w10447 (Browse shelf(Opens below))	Not For Loan

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April 2004.

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&P500 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.