Asset allocation using reliability method

This paper proposes a numerical method for the asset allocation problem based on the conventional Advanced First-Order Second Moment (AFOSM) reliability analysis. The proposed method separates the space of decision problems from the space of uncertain returns. By this separation, an uncertain asset...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical and computer modelling 2009-07, Vol.50 (1), p.21-31
Main Authors: Hanafizadeh, P., Ponnambalam, K.
Format: Article
Language:English
Subjects:
Advanced first-order second moment
Asset allocation
Calculus of variations and optimal control
Combinatorics
Combinatorics. Ordered structures
Designs and configurations
Exact sciences and technology
Mathematical analysis
Mathematics
Methods of scientific computing (including symbolic computation, algebraic computation)
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Probabilistic optimization
Sciences and techniques of general use
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a numerical method for the asset allocation problem based on the conventional Advanced First-Order Second Moment (AFOSM) reliability analysis. The proposed method separates the space of decision problems from the space of uncertain returns. By this separation, an uncertain asset allocation problem can be converted into two recursive optimization problems. One is defined in the space of random returns and the other in the space of decision variables. The proposed method can accept general objective functions like nonlinear utility functions, nonlinear loss functions or nonlinear transaction costs. An adaptive algorithm is designed to find the solution of the recursive models. The convergence of the algorithm is also proven. Numerical illustrations are included in support of the theory and Monte Carlo simulation is used to evaluate the quality of solutions.
ISSN:0895-7177
1872-9479
DOI:10.1016/j.mcm.2009.02.008