High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators-Reference-Cited by-同舟云学术

High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators

Published:2019-02-11 Issue:7 Volume:89 Page:1278-1300
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Choi Young-Geun¹,Lim Johan¹,Choi Sujung²

Affiliation:

1. Department of Statistics, Seoul National University, Seoul, Korea

2. School of Business, Soongsil University, Seoul, Korea

Funder

National Research Foundation of Korea

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2019.1577855

Reference35 articles.

1. On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model

2. Choi Y-G, Lim J, Roy A, et al. Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage. J Multivar Anal. 2018. (To appear). Arxiv version is available from https://arxiv.org/abs/1606.03814.

3. Large covariance estimation by thresholding principal orthogonal complements

4. A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees

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

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