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Size matters: optimal calibration of shrinkage estimators for portfolio selection

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Size matters : optimal calibration of shrinkage estimators for portfolio selection. / DeMiguel, Victor; Martin Utrera, Alberto; Nogales, Francisco J. .

In: Journal of Banking and Finance, Vol. 37, No. 8, 08.2013, p. 3018-3034.

Research output: Contribution to journalJournal articlepeer-review

Harvard

DeMiguel, V, Martin Utrera, A & Nogales, FJ 2013, 'Size matters: optimal calibration of shrinkage estimators for portfolio selection', Journal of Banking and Finance, vol. 37, no. 8, pp. 3018-3034. https://doi.org/10.1016/j.jbankfin.2013.04.033

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Author

DeMiguel, Victor ; Martin Utrera, Alberto ; Nogales, Francisco J. . / Size matters : optimal calibration of shrinkage estimators for portfolio selection. In: Journal of Banking and Finance. 2013 ; Vol. 37, No. 8. pp. 3018-3034.

Bibtex

@article{b19391a54fc74fc2a41f37133506ac8d,
title = "Size matters: optimal calibration of shrinkage estimators for portfolio selection",
abstract = "We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters-the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.",
keywords = "Portfolio choice, Estimation error, Shrinkage intensity, Out-of-sample evaluation , Bootstrap",
author = "Victor DeMiguel and {Martin Utrera}, Alberto and Nogales, {Francisco J.}",
year = "2013",
month = aug,
doi = "10.1016/j.jbankfin.2013.04.033",
language = "English",
volume = "37",
pages = "3018--3034",
journal = "Journal of Banking and Finance",
issn = "0378-4266",
publisher = "Elsevier",
number = "8",

}

RIS

TY - JOUR

T1 - Size matters

T2 - optimal calibration of shrinkage estimators for portfolio selection

AU - DeMiguel, Victor

AU - Martin Utrera, Alberto

AU - Nogales, Francisco J.

PY - 2013/8

Y1 - 2013/8

N2 - We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters-the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.

AB - We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters-the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.

KW - Portfolio choice

KW - Estimation error

KW - Shrinkage intensity

KW - Out-of-sample evaluation

KW - Bootstrap

U2 - 10.1016/j.jbankfin.2013.04.033

DO - 10.1016/j.jbankfin.2013.04.033

M3 - Journal article

VL - 37

SP - 3018

EP - 3034

JO - Journal of Banking and Finance

JF - Journal of Banking and Finance

SN - 0378-4266

IS - 8

ER -