The Markowitz Optimization Enigma: Is 'Optimized' Optimal?

By Richard O. Michaud. 2005

1,471 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9b0e618c73b16af2ea29c1fa5e9ca39b9a120047

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Citations in the corpus

Application of robust statistics to asset allocation models (2006, neutral): “AMS Subject Classification: • 62G35, 62P20, 91B28.”
Computing efficient frontiers using estimated parameters (1993, background): “Michaud [12] discusses the implications o f estimation error for portfolio managers.”
Large Dynamic Covariance Matrices (2019, background): “This observation goes back at least to Michaud (1989), who famously described portfolio optimization as “error maximization.”
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, background): “Consequently, mean forecasts are subject to substantial prediction errors which in turn can completely dominate and distort the analysis (e.g. Michaud, 1989).”
The case for REITs in the mixed-asset portfolio in the short and long Run (2005, background): “Michaud (1989, 1998) argues that optimisers are essentially “estimation-error maximisers” in that risk and return inputs are invariably subject to estimation error, even using historic data.”
ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY (2009, background): “We also note that Michaud (1989) and others conclude that the estimator R̂p in the MV optimization procedure generates large estimation error.”
Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns (2009, neutral): “This is not only a formidable econometric problem given the large number of moments involved and the need to ensure the positive deﬁniteness of the covariance matrix, but the results of the procedure are also notoriously noisy and unstable (e.g., Michaud 1989).”
Portfolio optimization with conditional value-at-risk objective and constraints (2001, methodology): “The first constraint (29) is the budget constraint; (30) requires portfolio’s expected return to be equal to a prescribed value rp; finally, (31) imposes bounds on portfolio weights, where νi are the same as in (23).”
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks (2017, background): “The number of degrees of freedom has to be an order of magnitude smaller than N2, or else portfolio selection inevitably turns into what Michaud (1989) calls “error maximization”.”
On portfolio optimization: Imposing the right constraints (2013, neutral): “1 See Frost and Savarino (1986), Jorion (1986), Michaud (1989), and Black and Litterman (1992), among others.”
A blocking and regularization approach to high dimensional realized covariance estimation (2012, neutral): “Estimating asset return covariances is indispensable in many areas in financial practice, such as portfolio management, risk management and asset pricing, (e.g., Michaud, 1989; Duffie and Pan, 1997; Chan, Karceski, and Lakonishok, 1999; Jagannathan and Ma, 2003).”
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, background): “In the context of financial portfolio theory it has even been observed that estimation errors in the input parameters of an optimization problem are often amplified by the optimization [23, 59].”
Centre De Referència En Economia Analítica Barcelona Economics Working Paper Series Working Paper Nº 92 Honey, I Shrunk the Sample Covariance Matrix Honey, I Shrunk the Sample Covariance Matrix (neutral): “Michaud (1989) calls this phenomenon " errormaximization " .”
Stable distributions in the Black–Litterman approach to asset allocation (2007, background): “mean, see Kallberg and Ziemba [16], [17], Best and Grauer [3], Michaud [ 21 ])”