Robust portfolio risk minimization using the graphical lasso
Robust portfolio risk minimization using the graphical lasso
We apply the statistical technique of graphical lasso for inverse covariance estimation of asset price returns in Markowitz portfolio optimisation. Graphical lasso induces sparsity in the inverse covariance matrix, thereby capturing conditional independences between different assets. We show empirical results that not only the resulting minimum risk portfolio is robust, in that the variation in expected returns is reduced when a fraction of the data is assumed missing, but also enables the construction of a financial network in which groups of assets belonging to the same financial sector are linked.
Covariance Estimation, portfolio optimization, graphical lasso, financial networks, Graphical model
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
2017
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Record type:
Conference or Workshop Item
(Paper)
Abstract
We apply the statistical technique of graphical lasso for inverse covariance estimation of asset price returns in Markowitz portfolio optimisation. Graphical lasso induces sparsity in the inverse covariance matrix, thereby capturing conditional independences between different assets. We show empirical results that not only the resulting minimum risk portfolio is robust, in that the variation in expected returns is reduced when a fraction of the data is assumed missing, but also enables the construction of a financial network in which groups of assets belonging to the same financial sector are linked.
Text
Robust Portfolio Risk Minimization Using the Graphical Lasso
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Accepted/In Press date: 22 August 2017
Published date: 2017
Keywords:
Covariance Estimation, portfolio optimization, graphical lasso, financial networks, Graphical model
Identifiers
Local EPrints ID: 414916
URI: http://eprints.soton.ac.uk/id/eprint/414916
PURE UUID: d2f6580b-5e9f-4eb4-9069-93236cec84a6
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Date deposited: 16 Oct 2017 16:30
Last modified: 16 Mar 2024 03:55
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Author:
Tristan Millington
Author:
Mahesan Niranjan
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