Dynamic portfolio optimization with risk control for absolute deviation model

In this paper, we present a new multiperiod portfolio selection with maximum absolute deviation model. The investor is assumed to seek an investment strategy to maximize his/her terminal wealth and minimize the risk. One typical feature is that the absolute deviation is employed as risk measure inst...

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Bibliographic Details
Published in:European journal of operational research Vol. 201; no. 2; pp. 349 - 364
Main Authors: Yu, Mei, Takahashi, Satoru, Inoue, Hiroshi, Wang, Shouyang
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.03.2010
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Absolute deviation
Applied sciences
Dynamic programming
Exact sciences and technology
Investment policy
Linear programming
Mathematical programming
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Portfolio management
Portfolio optimization
Portfolio optimization Linear programming Absolute deviation Dynamic programming
Portfolio theory
Risk aversion
Risk theory. Actuarial science
Studies
Portfolio optimization
Linear programming
Absolute deviation
Dynamic programming
Modeling
Terminal
Dynamic method
Exact solution
Optimal strategy
Portfolio selection
Risk management
Portfolio management
Investment
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:In this paper, we present a new multiperiod portfolio selection with maximum absolute deviation model. The investor is assumed to seek an investment strategy to maximize his/her terminal wealth and minimize the risk. One typical feature is that the absolute deviation is employed as risk measure instead of classical mean variance method. Furthermore, risk control is considered in every period for the new model. An analytical optimal strategy is obtained in a closed form via dynamic programming method. Algorithm with some examples is also presented to illustrate the application of this model.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2009.03.009