A VaR Black–Litterman model for the construction of absolute return fund-of-funds

DOI10.1080/14697680903121018zbMath1258.91200OpenAlexW2032278446MaRDI QIDQ4911225

Miguel A. Lejeune

Publication date: 14 March 2013

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697680903121018

zbMATH Keywords

portfolio optimizationprobabilistic programmingtrading constraintsBlack-Littermanabsolute returnfunds-of-funds

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Stochastic programming (90C15) Portfolio theory (91G10)

Related Items (9)

Distributionally robust portfolio optimization with linearized STARR performance measure ⋮ Risk-return trade-off with the scenario approach in practice: a case study in portfolio selection ⋮ Complex portfolio selection via convex mixed‐integer quadratic programming: a survey ⋮ A bi‐level programming framework for identifying optimal parameters in portfolio selection ⋮ Stochastic portfolio optimization with proportional transaction costs: convex reformulations and computational experiments ⋮ A smooth non-parametric estimation framework for safety-first portfolio optimization ⋮ Threshold Boolean form for joint probabilistic constraints with random technology matrix ⋮ Warm-start heuristic for stochastic portfolio optimization with fixed and proportional transaction costs ⋮ Game Theoretical Approach for Reliable Enhanced Indexation

Uses Software

• AMPL
• RiskMetrics

Cites Work

• Convex measures of risk and trading constraints
• Robust asset allocation
• Portfolio optimization with linear and fixed transaction costs
• Conditional and dynamic convex risk measures
• Coherent Measures of Risk
• An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints
• Updating the Inverse of a Matrix
• LP solvable models for portfolio optimization: a classification and computational comparison
• A Stochastic Programming Model
• Higher moment coherent risk measures
• Stable distributions in the Black–Litterman approach to asset allocation
• Robust Portfolio Selection Problems
• Benchmarking optimization software with performance profiles.
• Robust portfolio selection using linear-matrix inequalities

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