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Am Econ Rev 91(1):79–98īen-Tal A, Nemirovski AS (1998) Robust convex optimization. Financ Mark Portf Manag 21(2):147–166īenartzi S, Thaler RH (2001) Naive diversification strategies in defined contribution saving plans. Financ Mark Portf Manag 27(2):217–251Īnderson R, Bianchi M, Stephan W, Goldberg RL (2012) Will my risk parity strategy outperform? Financ Anal J 68(6):75–93īeach SL, Orlov AG (2007) An application of the Black–Litterman model with EGARCH-M derived views for international portfolio management. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.įinance: portfolio optimization Programming: inverse optimization Statistics: estimation.Allaj E (2013) The Black–Litterman model: a consistent estimation of the parameter tau. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. 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. 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. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. This insight allows us to significantly expand the scope and applicability of the BL model. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. In this paper, we provide a new perspective. The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry.

Black litterman reverse optimization