Improved estimation of the covariance matrix of stock returns with an application to portfolio selection

By Olivier Ledoit, Michael Wolf. 2003

1,648 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b53bcfa7ce96fad265131c3d336075d1f8a573f6

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Citations in the corpus

Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, neutral): “The third set of approaches are Bayesian and consist in shrinkage of the covariance estimates (Ledoit and Wolf, 2003), assuming a prior distribution for expected returns and covariances and reformulating the Markowitz problem as a stochastic optimization one (Lai et al.”
Multivariate Shrinkage for Optimal Portfolio Weights (2007, methodology): “Jorion (1986), and Ledoit and Wolf (2003) propose portfolio procedure improvements via shrinkage estimators, optimizing the trade-off between sample moments and shrinkage targets.”
Principles of minimum variance robust adaptive beamforming design (2013, background): “Note that other more sophisticated estimates of the data covariance matrix than (7) can be used [84,85].”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, positive): “Second, we describe a route to obtain improved estimates of the covariance matrix via shrinkage combined with analytic determination of the shrinkage intensity according to the Ledoit-Wolf theorem (Ledoit and Wolf, 2003).”
Estimating High Dimensional Covariance Matrices and its Applications (2011, methodology): “A consistent estimator α̂∗ for α∗ is derived in Ledoit and Wolf (2003), the details are omitted.”
Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection (2010, methodology): “…(2007).9 Certainty equivalents and monetary utility gains are reminiscent of the realized standard deviation comparison reported in Chan, Karceski, and Lakonishok (1999), Ledoit and Wolf (2003), and Jagannathan and Ma (2003), where preferences for higher-order moments are not taken into account.”
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions (2017, background): “Stock similarity is important for many investment decision strategies [3].”
Cleaning large correlation matrices: tools from random matrix theory (2016, methodology): “The linear shrinkage estimator uses a shrinkage intensity α estimated from the data following [154] (LW).”
Dominating estimators for minimum-variance portfolios (2010, methodology): “…minimum-variance portfolios have been frequently advocated in the literature (Frahm, 2008; Jagannathan and Ma, 2003; Kempf and Memmel, 2006; Ledoit and Wolf, 2003) because they are completely independent of the expected asset returns, which have been found to be the principal source of…”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “…as the number of observations—has experienced a large expansion over the last decade or so and is still growing; to list only a few examples, see Ledoit and Wolf (2003); Jagannathan and Ma (2003); Fan, Fan, and Lv (2008); DeMiguel et al. (2009a); Fan, Liao, and Mincheva (2013); DeMiguel,…”
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): “Ledoit and Wolf (2003) suggest the single-factor matrix of Sharpe (1963) as the shrinkage target.”
Noise sensitivity of portfolio selection under various risk measures (2006, methodology): “These filtering procedures include single- and multifactor models, (for a review, see e.g. Elton and Gruber, 1995), Bayesian estimators (e.g. Jorion, 1986; Frost and Savarino, 1986; Ledoit and Wolf, 2003), and more recently, tools taken over from random matrix theory (Laloux et al., 2000).”
It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification (2012, background): “We can also reduce the impact of the information 9Some recent examples of work in this area include Pastor (2000), Pastor and Stambaugh (2000), MacKinlay and Pastor (2000), Jagannathan and Ma (2003), Ledoit and Wolf (2003), (2004), Garlappi, Uppal, and Wang (2007), and Kan and Zhou (2007).”
Financial Valuation and Risk Management Working Paper No . 454 CHICAGO : A Fast and Accurate Method for Portfolio Risk Calculation (2008, methodology): “Martin (2006) shows that E[X|X ≤ c] ≈ E[X] − c− E[X] t̂ f̂X(c) F̂X(c) .”