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Table 1.9, <strong>Page</strong> 53Table: Solutions of penalized mean-variance optimizationMVO Ridge Lasso(NC) (C) (S) (D) (S) (D)x ⋆ 1 112.29 62.09 38.88 51.62 24.41 25.00x ⋆ 2 48.30 14.17 28.06 36.85 11.36 25.00x ⋆ 3 48.10 62.21 27.34 29.34 27.78 25.00x ⋆ 4 −39.69 −38.48 −1.57 −0.47 0.00 20.42Introduction to Risk Parity and Budgeting Modern Portfolio Theory 28 / 40
Figure 1.18, <strong>Page</strong> 54Figure: Weights of penalized MVO portfolios (in %)Introduction to Risk Parity and Budgeting Modern Portfolio Theory 29 / 40
Page 1 and 2: Introduction to Risk Parity and Bud
Page 3 and 4: Figure 1.2, Page 8Figure: The effic
Page 5 and 6: Tables 1.2 & 1.3, Page 8Table: Solv
Page 7 and 8: Table 1.4, Page 10Table: Solving th
Page 9 and 10: Figure 1.5, Page 15Figure: The effi
Page 11 and 12: Figure 1.6, Page 20Figure: The effi
Page 13: Table 1.7, Page 26Table: Black-Litt
Page 16 and 17: Figure 1.9, Page 31Figure: Density
Page 18 and 19: Figure 1.11, Page 35Figure: Cumulat
Page 20 and 21: Figure 1.13, Page 38Figure: Density
Page 22 and 23: Result (1.21), Page 39Ĉ =⎛⎜⎝
Page 24 and 25: Figure 1.16, Page 46Figure: Uncerta
Page 26 and 27: Result (1.23), Page 49Ĉ =⎛⎜⎝
Page 30 and 31: Tables 1.10 & 1.11, Page 55Table: P
Page 32 and 33: Table 1.12, Page 57Table: Effect of
Page 34 and 35: Page 62Number of solved QP problems
Page 36 and 37: Table 1.15, Page 63Table: Sampling
Page 38 and 39: Figure 1.20, Page 65Figure: Samplin
Page 40: Tables 1.19 & 1.20, Pages 69 & 70Ta

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