Adverse risk incentives and the design of performance-bases contracts
Management Science
Robust portfolio selection problems
Mathematics of Operations Research
Perspective cuts for a class of convex 0–1 mixed integer programs
Mathematical Programming: Series A and B
Portfolio Optimization with Factors, Scenarios, and Realistic Short Positions
Operations Research
Index tracking with constrained portfolios: Research Articles
International Journal of Intelligent Systems in Accounting and Finance Management
Journal of Global Optimization
Lagrangian relaxation procedure for cardinality-constrained portfolio optimization
Optimization Methods & Software
Perspective reformulations of mixed integer nonlinear programs with indicator variables
Mathematical Programming: Series A and B - Series B - Special Issue: Combinatorial Optimization and Integer Programming
A computational study on robust portfolio selection based on a joint ellipsoidal uncertainty set
Mathematical Programming: Series A and B
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We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is required to be below a low-risk threshold with a large probability, thereby limiting the market risk exposure of the investors. The resulting stochastic integer problem is reformulated through the derivation of a deterministic equivalent for the risk constraint and the use of a block decomposition technique. We develop an exact outer approximation method based on the relaxation of some binary restrictions and the reformulation of the cardinality constraint. The method provides a hierarchical organization of the computations with expanding sets of integer-restricted variables and outperforms the Bonmin and the CPLEX solvers. The method can solve large instances up to 1,000 securities, converges fast, scales well, and is general enough to be applicable to problems with buy-in-threshold constraints.