We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying...

Quantitative asset allocation models have not been widely adopted by practitioners because they suffer from two problems: the lack of robustness and diversification of portfolios obtained through these models. To solve these problems, I developed a new portfolio selection method that can be...

We study the distributionally robust stable tail adjusted return ratio (DRSTARR) portfolio optimization problem, in which the objective is to maximize the STARR performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain...

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...

This article presents a new approach for building robust portfolios based on stochastic efficiency analysis and periods of market downturn. The empirical analysis is done on assets traded on the Brazil Stock Exchange, B3 (Brasil, Bolsa, Balcão). We start with information on the assets from...

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 improvements apply also in the other optimisation...

We develop a robust optimal dynamic hedging strategy that takes both downside risks and market incompleteness into account for an agent who fears model misspecification. The robust agent is assumed to minimize the shortfall between the assets and liabilities under an endogenous worst case...

We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a φ-divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean-variance problem. We also show that while...

Many investors use optimization to determine their optimal investment portfolio. Unfortunately, optimal portfolios are sensitive to changing input parameters, i.e., they are not robust. Traditional robust optimization approaches aim for an optimal and robust portfolio which, ideally, is the...

I entertain a generalization of the standard Bolzmann-Gibbs-Shannon measure of entropy in multiplier preferences of model uncertainty. Using this measure, I derive a generalized exponential certainty equivalent, which nests the exponential certainty equivalent of the standard Hansen-Sargent...