Optimizing Optimization: The Next Generation of Optimization Applications and Theory (Quantitative Finance)

302 Index

D
Data reweighting , 261 – 2
Decay factor ρ , 266
Disappointment aversion (DA) , 258
coeffi cient of , 268
Dow Jones Industrial Average (DJIA)
components
empirical evidence from , 119 – 21
Downside and upside statistics, analytic
expression for , 175
optimization and portfolio selection ,
175
Downside risk , 143
asset allocation problem , 150 – 3
empirical illustration , 153 – 8
measuring with , 145 – 50
EVaR and VaR , 145 – 50
CVaR , 288 – 92
DTR ™ optimizer , 167 – 70 , 171 , 176
α added value representation , 169

E
Economic axioms , 283 – 4
Efron, Bradley , 163
Ellipticity, role of
optimal mean/downside risk frontiers,
in computing , 179
case of two assets , 184 – 90
conic results , 190 – 4
main proposition , 180 – 4
simulation methodology , 194 – 8
Endowment Consumption Rate (ECR) ,
83 , 84 , 85
Endowments , 78
decision making, under uncertainty ,
83 – 4
managing , 79 – 80
risk aversion, practical implications of ,
84 – 6
specifi cation , 80 – 2
trustees ’ attitude to risk , 82 – 3
types , 80
Equity portfolios, modeling, estimation,
and optimization of , 117
Dow Jones Industrial Average (DJIA)
components, empirical evidence
from , 119 – 21
portfolio selection problem , 130
performance ratios, review of , 132 – 4

portfolio strategies, empirical

comparison among , 134 – 6 , 137 – 9

scenarios coherent with empirical

evidence, generation of , 121

portfolio dimensional problem ,

121 – 5

return scenarios, generation of ,

126 – 30

Estimation error , 4 , 5 , 12 , 225 , 292 – 3

Expected tail loss (ETL) , 133

Expected utility , 80 , 83 , 168 , 249 , 269 ,

296 , 298

Expectile value at risk (EVaR) , 158

advantages , 146

defi nition and properties , 145 – 7

modeling dynamically , 147 – 50

Explicit risk budgeting , 65 – 6

Extreme value of annual returns , 173

F

Forecast error (FE) , 59

BITA robust applications, in

controlling , 61

constraints , 61 – 2

standard deviation , 65

First passage time probability , 99

Forsey-Sortino Optimizer , 162

basic assumptions , 162 – 5

optimize/measure performance , 165 – 6

Full-scale optimization, of WPA , 94 ,

111 – 12

for log-wealth investor , 105

problem , 104

WPA solution , 104 – 7 , 108 , 109 , 110

Fund of funds , 18 – 22

Fundamental risk model

constraining active risk with , 27

G

Gain-Loss Optimization (GLO) , 54 , 66

adding 25% investment constraint , 70

analysis and comparison , 69

choice of inputs , 68 – 9

emerging market returns, down-

trimming of , 70 – 1

maximum holding , 70

and omega , 67 – 8

squared losses , 71 – 2

GARCH model , 118 , 120 , 212 , 251 , 295