Operations Research
Convex Optimization
Selected topics in robust convex optimization
Mathematical Programming: Series A and B
Constructing Risk Measures from Uncertainty Sets
Operations Research
Constructing Uncertainty Sets for Robust Linear Optimization
Operations Research
Theory and Applications of Robust Optimization
SIAM Review
Inverse conic programming with applications
Operations Research Letters
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The Black-Litterman BL model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a mean variance inverse optimization MV-IO portfolio and a robust mean variance inverse optimization RMV-IO portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward trade-off than their BL counterparts and are more robust to incorrect investor views.