Improving Portfolios Global Performance with Robust Covariance Matrix Estimation: Application to the Maximum Variety Portfolio

Autor: Jay, Emmanuelle, Terreaux, Eugénie, Ovarlez, Jean-Philippe, Pascal, Frédéric

This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same imp

Externí odkaz: http://arxiv.org/abs/1804.00191

��pisode 2 : Quand le contr��le prend le dessus

Autor: Jay, Emmanuelle, Scali, Emma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a19b80168e23e17be6b15a8e78a9fc84

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5 - Robust Portfolio Allocation with Systematic Risk Contribution Restrictions

Autor: Darolles, Serge ^*, Gouriéroux, Christian ^°, Jay, Emmanuelle

Publikováno v: In Risk-Based and Factor Investing 2016:123-146

Factor Selection

Autor: Darolles, Serge, Duvaut, Patrick, Jay, Emmanuelle

Publikováno v: Multi-factor models and signal processing techniques: application to quantitative finance
Multi-factor models and signal processing techniques: application to quantitative finance, pp.23-58, 2013, ⟨10.1002/9781118577387.ch2⟩
Multi-Factor Models and Signal Processing Techniques ISBN: 9781118577387

This chapter focuses on the empirical ad hoc approach and presents three reference models that are widely used in the literature. These models are all based on the factor representation, but highlight the nature of the factors to be used to explain s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08baced2e69f06927d5202e0189c13f9
https://hal.archives-ouvertes.fr/hal-01632873