@inproceedings{d602da88135c4248b6cc1c78e0206ec7,
title = "Robust Portfolio Risk Minimization Using the Graphical Lasso",
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.",
keywords = "Covariance estimation, Financial network, Graphical lasso, Graphical model, Portfolio optimization",
author = "Tristan Millington and Mahesan Niranjan",
note = "Funding Information: Acknowledgements. This work was partially funded by the Engineering and Physical Sciences Research Council, UK (EP/N014189: Joining the Dots, from Data to Insight). Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
month = oct,
day = "26",
doi = "10.1007/978-3-319-70096-0_88",
language = "English",
isbn = "9783319700953",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "863--872",
editor = "Dongbin Zhao and El-Alfy, {El-Sayed M.} and Derong Liu and Shengli Xie and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "United Kingdom",
}