Extracting from the relaxed for large-scale semi-continuous variable nondominated frontiers

Because of size and covariance matrix problems, computing much of anything along the nondominated frontier of a large-scale (1000–3000 securities) portfolio selection problem with semi-continuous variables is a task that has not previously been achieved. But given (a) the speed at which the nondominated frontier of a classical portfolio problem can now be computed and (b) the possibility that there might be overlaps between the nondominated frontier of the classical problem and that of the same problem but with semi-continuous variables, the paper shows how considerable amounts of the nondominated frontier of a large-scale mean-variance portfolio selection problem with semi-continuous variables can be computed in very little time.

[1]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[2]  Hiroshi Konno,et al.  Portfolio optimization under D.C. transaction costs and minimal transaction unit constraints , 2002, J. Glob. Optim..

[3]  G. Mitra,et al.  Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints , 2001 .

[4]  Andras Niedermayer,et al.  Applying Markowitz's Critical Line Algorithm , 2007 .

[5]  George Mavrotas,et al.  Multiobjective portfolio optimization with non-convex policy constraints: Evidence from the Eurostoxx 50 , 2014 .

[6]  Yue Qi,et al.  Randomly generating portfolio-selection covariance matrices with specified distributional characteristics , 2007, Eur. J. Oper. Res..

[7]  Mike C. Bartholomew-Biggs,et al.  A global optimization problem in portfolio selection , 2009, Comput. Manag. Sci..

[8]  Chang-Chun Lin,et al.  Genetic algorithms for portfolio selection problems with minimum transaction lots , 2008, Eur. J. Oper. Res..

[9]  Yue Qi,et al.  Comparative issues in large-scale mean-variance efficient frontier computation , 2011, Decis. Support Syst..

[10]  W. Sharpe Portfolio Theory and Capital Markets , 1970 .

[11]  H. Markowitz The optimization of a quadratic function subject to linear constraints , 1956 .

[12]  C. Lucas,et al.  Heuristic algorithms for the cardinality constrained efficient frontier , 2011, Eur. J. Oper. Res..

[13]  Clara Calvo,et al.  On the Computation of the Efficient Frontier of the Portfolio Selection Problem , 2012, J. Appl. Math..

[14]  Efstratios N. Pistikopoulos,et al.  Multiparametric linear and quadratic programming. , 2014 .

[15]  Maria Grazia Speranza,et al.  Heuristic algorithms for the portfolio selection problem with minimum transaction lots , 1999, Eur. J. Oper. Res..

[16]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[17]  Yue Qi,et al.  Large-scale MV efficient frontier computation via a procedure of parametric quadratic programming , 2010, Eur. J. Oper. Res..

[18]  HIROSHI KONNO,et al.  Global Optimization Versus Integer Programming in Portfolio Optimization under Nonconvex Transaction Costs , 2005, J. Glob. Optim..

[19]  Konstantinos P. Anagnostopoulos,et al.  Multiobjective evolutionary algorithms for complex portfolio optimization problems , 2011, Comput. Manag. Sci..

[20]  Jürgen Branke,et al.  Efficient implementation of an active set algorithm for large-scale portfolio selection , 2008, Comput. Oper. Res..

[21]  Miguel A. Lejeune,et al.  An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints , 2009, Oper. Res..

[22]  Hiroshi Konno,et al.  Integer programming approaches in mean-risk models , 2005, Comput. Manag. Sci..

[23]  Yazid M. Sharaiha,et al.  Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..