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  • Search: subject:"large random matrix"
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Year of publication
Subject
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Bootstrap Method 2 Large Random Matrix 2 Markowitz mean-variance optimization 2 Optimal Portfolio Allocation 2 Optimal Return 2 Bootstrap approach 1 Bootstrap-Verfahren 1 Correlation 1 Extreme eigenvalues 1 Finite rank perturbations 1 Korrelation 1 Large random matrix theory 1 MUSIC algorithm 1 Markowitz Mean-Variance Optimization 1 Mathematical programming 1 Mathematische Optimierung 1 Monte Carlo simulation 1 Monte-Carlo-Simulation 1 Portfolio selection 1 Portfolio-Management 1 Spectrally-corrected Covariance Matrix 1 bootstrap method 1 large random matrix 1 optimal portfolio allocation 1 optimal return 1 spectrally-corrected covariance matrix 1
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Online availability
All
Free 3 Undetermined 1
Type of publication
All
Book / Working Paper 3 Article 1
Type of publication (narrower categories)
All
Working Paper 2 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1
Language
All
English 2 Undetermined 2
Author
All
Bai, Zhidong 3 Li, Hua 3 McAleer, Michael 2 Wong, Wing-Keung 2 Hachem, Walid 1 Loubaton, Philippe 1 Mestre, Xavier 1 Najim, Jamal 1 Vallet, Pascal 1 Wong, Wing Keung 1
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Institution
All
Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München 1
Published in...
All
Discussion paper / Tinbergen Institute 1 Journal of Multivariate Analysis 1 MPRA Paper 1 Tinbergen Institute Discussion Paper 1
Source
All
RePEc 2 ECONIS (ZBW) 1 EconStor 1
Showing 1 - 4 of 4
Cover Image
Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization
Bai, Zhidong; Li, Hua; McAleer, Michael; Wong, Wing-Keung - 2016
This paper considers the portfolio problem for high dimensional data when the dimension and size are both large.We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main factor to...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011526102
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Cover Image
Spectrally-corrected estimation for high-dimensional Markowitz mean-variance optimization
Bai, Zhidong; Li, Hua; McAleer, Michael; Wong, Wing Keung - 2016
This paper considers the portfolio problem for high dimensional data when the dimension and size are both large.We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main factor to...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011456708
Saved in:
Cover Image
The best estimation for high-dimensional Markowitz mean-variance optimization
Bai, Zhidong; Li, Hua; Wong, Wing-Keung - Volkswirtschaftliche Fakultät, … - 2013
The traditional(plug-in) return for the Markowitz mean-variance (MV) optimization has been demonstrated to seriously overestimate the theoretical optimal return, especially when the dimension to sample size ratio $p/n$ is large. The newly developed bootstrap-corrected estimator corrects the...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011109231
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Cover Image
A subspace estimator for fixed rank perturbations of large random matrices
Hachem, Walid; Loubaton, Philippe; Mestre, Xavier; … - In: Journal of Multivariate Analysis 114 (2013) C, pp. 427-447
grow to infinity at the same pace. In the area of large random matrix theory, recent contributions studied the behavior of …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010594219
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