A simple parallel algorithm for large-scale portfolio problems

Purpose - Although the mean-variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large-scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard devia...

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Vydáno v:The journal of risk finance Ročník 11; číslo 5; s. 481 - 495
Hlavní autoři: Smimou, Kamal, Thulasiram, Ruppa K
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Emerald Group Publishing Limited 09.11.2010
Emerald
Emerald Group Publishing, Ltd
Témata:
Algorithms
Analysis
Capital assets
Economic models
Evaluation
Expected values
Linear programming
Mathematical optimization
Maßzahl
Nichtlineare Optimierung
Optimization
Portfolio management
Portfolio-Investition
Random variables
Standard deviation
Statistical methods
Theorie
Optimization techniques
Portfolio investment
Modelling
Programming and algorithm theory
ISSN:1526-5943, 2331-2947
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Shrnutí:Purpose - Although the mean-variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large-scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard deviation risk) function and finite iteration technique to remove difficulties in quadratic programming.Design methodology approach - Using van de Panne' approach, this paper proposes a finite technique to optimize large-scale portfolio selection problem.Findings - The proposal of parallel algorithm structure to the model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process.Originality value - The proposal of parallel algorithm structure to the mean-variance portfolio selection model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. An empirical example that illustrates the application and benefits of the method is provided.
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ISSN:1526-5943
2331-2947
DOI:10.1108/15265941011092068