Optimal Portfolios for DC Pension under the Quadratic Utility Function

2011 ◽  
Author(s):  
Zhang Chubing ◽  
Hou Ru-jing
Keyword(s):  
Utility Function ◽  
Optimal Portfolios ◽  
Quadratic Utility

Local Approximation of the Efficient Frontier in Robust Design

1999 ◽  
Author(s):  
Jinhuan Zhang ◽  
Margaret M. Wiecek ◽  
Wei Chen
Keyword(s):  
Utility Function ◽  
Robust Design ◽  
Optimization Problems ◽  
Design Procedure ◽  
Efficient Frontier ◽  
Pareto Solution ◽  
Quadratic Utility ◽  
Weighted Sum Method ◽  
Quality Aspects ◽  
Design Solutions

Abstract The multiple quality aspects of robust design have brought more and more attention in the advancement of robust design methods. Neither the Taguchi’s signal-to-noise ratio nor the weighted-sum method is adequate in addressing designer’s preference in making tradeoffs between the mean and variance attributes. An interactive multiobjective robust design procedure that follows upon the developments on relating utility function optimization to a multiobjective programming method has been proposed by the authors. This paper is an extension of our previous work on this topic. It presents a formal procedure for deriving a quadratic utility function at a candidate solution as an approximation of the efficient frontier to explore alternative robust design solutions. The proposed procedure is investigated at different locations of candidate solutions, with different ranges of interest, and for efficient frontiers with both convex and nonconvex behaviors. This quadratic utility function provides a decision maker with new information regarding how to choose a most preferred Pareto solution. As an integral part of the interactive robust design procedure, the proposed method assists designers in adjusting the preference structure and exploring alternative efficient robust design solutions. It eliminates the needs of solving the original bi-objective optimization problem repeatedly using new preference structures, which is often a computationally expensive task for problems in a complex domain. Though demonstrated for robust design problems, the principle is also applicable to any bi-objective optimization problems.

scholarly journals ANALISIS PORTOFOLIO SAHAM LQ45 MENGGUNAKAN FUNGSI UTILITAS KUADRATIK

2013 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
KADEK FRISCA AYU DEVI ◽  
KOMANG DHARMAWAN ◽  
NI MADE ASIH
Keyword(s):  
Utility Function ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Risk Preference ◽  
Optimal Portfolio ◽  
Function Optimization ◽  
Risk Averse ◽  
Expected Return ◽  
Quadratic Utility ◽  
Return Variance

Utility function can use to give risk preference for investors who want to get the benefits gained meets investment targets. Quadratic utility functions on optimal portfolio is strongly influenced by the expected return and standard deviation. The establishment of optimal portfolios using a quadratic utility function optimization problems. Under the settlement portfolio optimization, the necessary data is expected return, variance, and variance covariance matrix. The optimal portfolio is affected by some factors Risky less Rate, risk aversion index, and Borrow Rate. The results of settlement portfolio optimization is obtaining the utility value while the relatively large changes influencing by risk averse index.

The Geometry of Multidimensional Quadratic Utility in Models of Parliamentary Roll Call Voting

2001 ◽  
Vol 9 (3) ◽  
pp. 211-226 ◽  
Author(s):  
Keith T. Poole
Keyword(s):  
Utility Function ◽  
Spatial Model ◽  
Geometric Interpretation ◽  
Roll Call ◽  
Choice Data ◽  
Roll Call Voting ◽  
Quadratic Utility ◽  
Likelihood Model ◽  
Ideal Points ◽  
The Difference

The purpose of this paper is to show how the geometry of the quadratic utility function in the standard spatial model of choice can be exploited to estimate a model of parliamentary roll call voting. In a standard spatial model of parliamentary roll call voting, the legislator votes for the policy outcome corresponding to Yea if her utility for Yea is greater than her utility for Nay. The voting decision of the legislator is modeled as a function of the difference between these two utilities. With quadratic utility, this difference has a simple geometric interpretation that can be exploited to estimate legislator ideal points and roll call parameters in a standard framework where the stochastic portion of the utility function is normally distributed. The geometry is almost identical to that used by Poole (2000) to develop a nonparametric unfolding of binary choice data and the algorithms developed by Poole (2000) can be easily modified to implement the standard maximum-likelihood model.

Credit Cards Scoring With Quadratic Utility Function

2002 ◽  
Author(s):  
Vladimir Bugera ◽  
Hiroshi Konno ◽  
Stanislav P. Uryasev
Keyword(s):  
Utility Function ◽  
Credit Cards ◽  
Quadratic Utility

Optimal Portfolios with Derivatives

CFA Digest ◽  
1998 ◽  
Vol 28 (3) ◽  
pp. 40-42
Author(s):  
John H. Earl
Keyword(s):  
Optimal Portfolios
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