Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -