A Practical Guide to Robust Portfolio Optimization

Posted: 8 Dec 2019 Last revised: 22 Dec 2020

See all articles by Chenyang Yin

Chenyang Yin

Quantitative Research Group; BNP Paribas Asset Management

Romain Perchet

BNP Paribas Asset Management

François Soupé

BNP Paribas Asset Management

Date Written: November 20, 2019

Abstract

Robust optimization considers uncertainty in inputs to address the shortcomings of mean-variance optimization. We investigate the mechanisms by which robust optimization achieves its goal and give practical guidance regarding its parametrization. We show that quadratic uncertainty sets are preferred to box uncertainty sets, that a diagonal uncertainty matrix with only variances should be used, and that the level of uncertainty can be chosen based on Sharpe ratios. We use examples with the proposed parametrization to show that robust optimization efficiently overcomes the weaknesses of mean-variance optimisation and can be applied in real investment problems like multi-asset portfolio management or robo-advising.

Keywords: Robust optimization, Portfolio construction, Mean-variance optimization, Multi-asset, Asset Allocation

JEL Classification: G11, C61

Suggested Citation

Yin, Chenyang and Perchet, Romain and Soupé, François, A Practical Guide to Robust Portfolio Optimization (November 20, 2019). Available at SSRN: https://ssrn.com/abstract=3490680 or http://dx.doi.org/10.2139/ssrn.3490680

Chenyang Yin (Contact Author)

Quantitative Research Group ( email )

Paris
France

BNP Paribas Asset Management ( email )

Paris
France

Romain Perchet

BNP Paribas Asset Management ( email )

14 rue Bergere
Paris, 75009
France

François Soupé

BNP Paribas Asset Management ( email )

14 rue Bergere
Paris, 75009
France

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