Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps

By Ravi Jagannathan, Tongshu Ma. 2003

1,642 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1923c2ff291e5540e9202c26dac570efb3e7885b

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Citations in the corpus

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 (positive): “Based on the recent paper by Jagannathan and Ma (2003), a portfolio manager facing a long-only constraint might hope that it yields reasonable performance.”
Vast Portfolio Selection with Gross-exposure Constraints (2012, positive): “As a generalization to the work by Markowitz (1952), Jagannathan and Ma (2003), and Fan (2007), our utility optimization problem with gross-exposure constraint is maxw E[U (wT R)] such that wT 1 = 1, ‖w‖1 ≤ c, Aw = a. (2.1) The utility function U (·) can also be replaced by any risk measures such…”
Portfolio Choice Problems (2010, methodology): “The idea of shrinkage estimation is attributed to James and Stein (1961), who noted that for N ≥ 3 independent normal random variables, the vector of sample means μ̄ is dominated in terms of joint mean-squared error by a convex combination of the sample means and a common constant μ0 (see also Efron”
Indexing and Statistical Arbitrage (2005, background): “This is consistent with the finding of Jagannathan and Ma [2003] that imposing constraints can lessen model errors arising from outliers in the sample.”
Properties of the Most Diversified Portfolio (2013, methodology): “Also, the long-only constraint has already an effect similar to using a robust estimation technique (see Jagannathan and Ma 2003).”
Sparse and Stable Portfolio Selection With Parameter Uncertainty (2015, neutral): “…usually brought by large estimation errors of unknown parameters rather than true parameter values of the population, correcting the portfolio weights directly amounts to reducing the impact of parameter uncertainty (Frost and Savarino 1986; Jagannathan and Ma 2003; Garlappi, Uppal, and Wang 2007).”
Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz (2015, background): “Jorion (1985, 1986, 1991) and Jagannathan and Ma (2003) find that the pure minimum variance (MV) portfolio may outperform the mean–variance efficient portfolio.”
Optimal estimation of a large-dimensional covariance matrix under Stein’s loss (2018, neutral): “desirable out-of-sample properties not only in terms of risk but also in terms of reward-to-risk (that is, in terms of the information ratio); for example, see Haugen and Baker [17], Jagannathan and Ma [19], and Nielsen and Aylursubramanian [35].”
Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches (2017, positive): “These suggestions range from imposing portfolio constraints (Frost and Savarino 1988; Jagannathan and Ma 2003; Behr, Guettler, and Miebs 2013) to the use of factor models (Chan, Karceski, and Lakonishok 1999), and Bayesian methods for estimating the MV input parameters (Jorion 1985, 1986; Pastor…”
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks (2017, neutral): “These findings are consistent with Jagannathan and Ma (2003) who demonstrate theoretically that imposing a no-short-sales constraint corresponds to an implicit shrinkage of the sample covariance matrix in the context of estimating the global minimum variance portfolio.”
Robust Performance Hypothesis Testing with the Variance (2011, neutral): “The GMV portfolio has received much renewed interest in the recent literature; for example, see Jagannathan and Ma (2003), Kempf and Memmel (2006), Garlappi et al. (2007), Elton et al. (2008), DeMiguel et al. (2009a), DeMiguel et al. (2009b), Candelou et al. (2010), and Güttler and Trübenbach…”