On the application of an Augmented Lagrangian algorithm to some portfolio problems
- 195 Downloads
Algencan is a freely available piece of software that aims to solve smooth large-scale constrained optimization problems. When applied to specific problems, obtaining a good performance in terms of efficacy and efficiency may depend on careful choices of options and parameters. In the present paper, the application of Algencan to four portfolio optimization problems is discussed and numerical results are presented and evaluated.
KeywordsConstrained optimization Augmented Lagrangian Portfolios Generalized Order-Value Optimization Conditional Value-at-Risk
The authors are thankful to the anonymous referees whose comments helped to improve the quality of this work.
- Bartholomew-Biggs M (2006) Nonlinear optimization with financial applications. Springer Science & Business Media, New YorkGoogle Scholar
- Conn AR, Gould NIM, Toint PhL (1992) Lancelot: a fortran package for large-scale nonlinear optimization (Release A), Springer Series in Computational Mathematics, vol 17. Springer-Verlag, New YorkGoogle Scholar
- Elahi Y, Aziz IA (2014) Mean-variance-CVaR model of multiportfolio optimization via linear weighted sum method. Math Prob Eng 2014 (Article ID 104064) Google Scholar
- Fourer R, Gay DM, Kernighan BW (2002) AMPL: a modeling language for mathematical programming. Boyd & Fraser Publishing Company, Danvers, MAGoogle Scholar
- GLPK (2015) GNU Project – Free Software Foundation (FSF). https://www.gnu.org/software/glpk/. Accessed 24 Feb 2015
- Jorion P (2007) Value at risk: the new benchmark for managing financial risk. McGraw-Hill, New YorkGoogle Scholar
- Kull M (2014) Portfolio optimization for constrained shortfall risk: Implementation and IT Architecture considerations, M.Sc. Thesis, ETH Zürich,Google Scholar
- Markowitz H (1952) Portfolio selection. J Finan 7:77–91Google Scholar
- Powell MJD (1969) A method for nonlinear constraints in minimization problems. In: Fletcher R (ed) Optimization. Academic Press, New York, NY, pp 283–298Google Scholar
- Uryasev S (2000) Conditional value-at-risk: optimization algorithms and applications, In: Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) 49–57Google Scholar
- Wang Y, Dang C, Wang S Robust novelty detection via worst case CVaR minimization, IEEE Transactions on Neural Networks and Learning Systems, to appear. doi: 10.1109/TNNLS.2014.2378270