Optimal Portfolio Choice with Parameter Uncertainty

By Raymond Kan, Guofu Zhou. Journal of Financial and Quantitative Analysis, 2007

653 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f8b16bcf1fabd56dd522baa3fbf3a3c2d9e7bb7d

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Citations in the corpus

Improving Mean Variance Optimization through Sparse Hedging Restrictions (2015, background): “…portfolio weights to perturbations in inputs can be on the order of 4,000 times greater in the 100-stock universe than in the 10-stock universe.4 Kan and Zhou (2007) also demonstrate that when N/T is not so small, estimation errors in return forecasts and covariances produce large interactive…”
Portfolio selection with higher moments (2010, neutral): “†Kan and Zhou ( 2007 ) provide a thorough discussion of the difference of plug-in and Bayes estimator of the optimal decision under the parameter-based utility ( 5 ).”
Should investors include Bitcoin in their portfolios? A portfolio theory approach (2020, methodology): “Inspired by Kan and Zhou (2007) and DeMiguel et al (2009), amongst others, and in an attempt to eliminate further the negative effects of estimation risk in the portfolio construction process, we also apply a method that shrinks the portfolio weights by considering combinations of portfolios.”
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks (2017, neutral): “1) is the same as one of the two shrinkage formulas obtained by Ledoit and Wolf (2012), even though the loss functions are completely different. We consider this to be reassuring because it is easier to trust a shrinkage formula that is optimal with respect to multiple loss functions than one which ”
It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification (2012, methodology): “Building on the idea that the 1/N portfolio is a reasonable shrinkage target, they propose a new strategy that optimally combines the 1/N portfolio and the Kan and Zhou (2007) 3-fund portfolio, with the rate of shrinkage toward the 1/N portfolio determined by the level of estimation risk.”
What Matters to Individual Investors? Evidence from the Horse's Mouth (2018, neutral): “Bayesian investors will reduce their allocation to the risky asset in the face of parameter uncertainty, and investors who are ambiguity averse in the sense of Ellsberg (1961) will reduce their risky allocation even further (Barberis (2000), Garlappi, Uppal, and Wang (2007), Kan and Zhou (2007)).”
Dominating estimators for minimum-variance portfolios (2010, methodology): “The shrinkage estimators presented by the aforementioned authors are determined by unknown quantities, which have to be estimated (see, e.g., Kan and Zhou, 2007).”
Optimal Shrinkage-Based Portfolio Selection in High Dimensions (2016, methodology): “Table 2: Performance of traditional, bona-fide, the benchmark portfolios (LWQuEST - Ledoit and Wolf (2017b), LWAnalytical - Ledoit and Wolf (2020), KZ - Kan and Zhou (2007)) and the target portfolios for the minimum variance calibration criteria.”
Optimal vs naïve diversification in cryptocurrencies (2018, background): “Black and Litterman (1992) and Kan and Zhou (2007) show that mean-variance struggles with estimation errors in the parameters of the model, while DeMiguel et al (2009) show that employing estimated means and variances performs worse than a naïve strategy of simply holding equal positions in every…”
Covariance Regression Analysis (2017, neutral): “However, ̂sam does not perform well as p → ∞ (Bai 1999), which can yield nonnegligible estimation errors (Kan and Zhou 2007).”
Learning in Financial Markets (2009, background): “…include Kandel and Stambaugh (1996), Barberis (2000), Pástor (2000), Pástor and Stambaugh (2000, 2002b), Tu and Zhou (2004, 2008), Avramov (2004), Brandt et al (2005), Avramov and Chordia (2006), Avramov and Wermers (2006), Kan and Zhou (2007), and Wachter and Warusawitharana (2009).”
ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY (2009, background): “3 See, for example, Zellner and Chetty (1965), Brown (1978), and Kan and Zhou (2007).”
Sparse and Stable Portfolio Selection With Parameter Uncertainty (2015, neutral): “Kan and Zhou (2007) also considered two other plug-in approaches, which estimate by ̄ = T T −1 ̂ and ̃ = T T −N−2 ̂, leading to the unbiased estimators of and w, respectively.”
Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix (2012, positive): “The first two are based on the shrinkage estimation of the covariance matrix developed in Ledoit and Wolf (2003) and in Ledoit and Wolf (2004a) respectively denoted with LW (1f) and LW (id).”
Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies ☆ (2011, methodology): “…we ask whether any of the four sophisticated rules, namely, the Markowitz rule as well as its extensions proposed by Jorion (1986), MacKinlay and Pástor (2000), and Kan and Zhou (2007), can be combined with the naive 1/N rule to obtain better portfolio rules that can perform consistently well.”