Garlappi, L., Uppal, R., & Wang, T. (2007). Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach. The Review of Financial Studies, 20, 41-81.
https://doi.org/10.1093/rfs/hhl003

has been cited by the following article:

• TITLE: Mean-Variance Portfolio Choice with Uncertain Variance-Covariance Matrix

  AUTHORS: Wei Guo, Yichao Wang, Danping Qiu

  KEYWORDS: Mean-Variance Portfolio, Uncertain Variance-Covariance

  JOURNAL NAME: Journal of Financial Risk Management, Vol.9 No.2, April 23, 2020

  ABSTRACT: Expected returns, variances, and co-variances are key inputs of mean-variance portfolio selection problems. In traditional mean-variance portfolio models, the model uncertainty is excluded a priori. But in practice, these parameters are not known a priori and are usually estimated with error. Current researches incorporate the model uncertainty into the mean-variance framework but mainly focus on the uncertain means. The aim of this dissertation is to incorporate uncertain variance-covariance into mean-variance portfolio model via the concept of ambiguity and ambiguity aversion. The approaches developed in this study numerically compare the impact from return ambiguity and variance ambiguity. In particular, re-examine if uncertain variance-covariance can lead to “No-Participation in Stock Market” and/or “Home Bias” via stock indexes data.