Figures & data
Fig. 1 Estimated optimal shrinkage intensities for and
(MP) as function of concentration ratio c > 1 and dimension p = 300.
![Fig. 1 Estimated optimal shrinkage intensities for Sn* and Sn+(MP) as function of concentration ratio c > 1 and dimension p = 300.](/cms/asset/c5493c0a-b8e1-4330-a74f-d30bd006958a/ubes_a_2004897_f0001_c.jpg)
Fig. 2 Estimated optimal shrinkage intensities for and
(MP) as function of dimension p for c = 1.5 (left) and c = 2 (right).
![Fig. 2 Estimated optimal shrinkage intensities for Sn* and Sn+(MP) as function of dimension p for c = 1.5 (left) and c = 2 (right).](/cms/asset/eaf18baf-a91f-4b00-9feb-1f120dd8447e/ubes_a_2004897_f0002_c.jpg)
Fig. 3 The asymptotic optimal shrinkage intensity as a function of c for the calibration criteria (i)-(ii) from Proposition 2.1 (left to right).
![Fig. 3 The asymptotic optimal shrinkage intensity as a function of c for the calibration criteria (i)-(ii) from Proposition 2.1 (left to right).](/cms/asset/d3e8fad2-7079-4ead-91f3-6e1cea00a14f/ubes_a_2004897_f0003_c.jpg)
Fig. 4 The relative losses for the portfolios based on the optimal shrinkage estimator, the traditional estimator and the equally weighted portfolio as a function of c for the calibration criteria (i)-(ii) from Proposition 2.1 (left to right). The dimension is set to p = 100 and the condition index is set to 1000.
![Fig. 4 The relative losses for the portfolios based on the optimal shrinkage estimator, the traditional estimator and the equally weighted portfolio as a function of c for the calibration criteria (i)-(ii) from Proposition 2.1 (left to right). The dimension is set to p = 100 and the condition index is set to 1000.](/cms/asset/7ee581fa-ff3b-473e-ad34-f43bdc1f86e1/ubes_a_2004897_f0004_c.jpg)
Fig. 5 The relative losses for the portfolios based in the optimal shrinkage estimator, the traditional estimator and the equally weighted portfolio as a function of the dimension p for 0.2 (top left), 0.5 (top right), 0.8 (bottom left), 2 (bottom right). The condition index is set to 1000 and the mean-variance calibration criteria is used.
![Fig. 5 The relative losses for the portfolios based in the optimal shrinkage estimator, the traditional estimator and the equally weighted portfolio as a function of the dimension p for c= 0.2 (top left), 0.5 (top right), 0.8 (bottom left), 2 (bottom right). The condition index is set to 1000 and the mean-variance calibration criteria is used.](/cms/asset/b1622a81-452d-4cbc-8511-26cdb6113ccd/ubes_a_2004897_f0005_c.jpg)
Table 1 Performance of traditional, bona-fide, the benchmark portfolios (LWQuEST— Ledoit and Wolf 2017b, LWAnalytical— Ledoit and Wolf 2020, KZ - Kan and Zhou Citation2007) and the target portfolios for the mean-variance calibration criteria.
Table 2 Performance of traditional, bona-fide, the benchmark portfolios (LWQuEST - Ledoit and Wolf 2017b, LWAnalytical - Ledoit and Wolf 2020, KZ - Kan and Zhou Citation2007) and the target portfolios for the minimum variance calibration criteria.
Fig. 6 The bona-fide shrinkage intensities for the first 100 assets (alphabetic order) using the equally weighted target portfolio and the mean-variance calibration for c = 0.2, 0.5, 0.8 and 2. Above - bona fide, below - bona fide ridge (see formula (2.33)).
![Fig. 6 The bona-fide shrinkage intensities for the first 100 assets (alphabetic order) using the equally weighted target portfolio and the mean-variance calibration for c = 0.2, 0.5, 0.8 and 2. Above - bona fide, below - bona fide ridge (see formula (2.33)).](/cms/asset/0965c99e-eb6a-4ede-9be8-c7af5d9025fe/ubes_a_2004897_f0006_c.jpg)