Data-Driven Optimization of Ambiguous Reward-Risk Ratio Measures

Ji, Ran; Lejeune, Miguel A.

doi:10.2139/ssrn.2707122

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…However, rates of convergence have only been established recently, see Fournier and Guilin [21]. While the relative entropy as a concept for closedness of models was introduced by Hansen and Sargent [25], Wasserstein neighborhoods were considered for the first time by Pflug and Wozabal [44] and recently followed by Esfahani and Kuhn [16] as well as Gao and Klowegt [22] and Ji and Lejeune [26]. In the remainder of this paper we restrict our considerations to ambiguity sets defined by Wasserstein neighborhoods.…”

Section: Ambiguity Setsmentioning

confidence: 99%

A Review on Ambiguity in Stochastic Portfolio Optimization

Pflug

Pohl

2017

Set-Valued Var. Anal

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In mean-risk portfolio optimization, it is typically assumed that the assets follow a known distribution P 0 , which is estimated from observed data. Aiming at an investment strategy which is robust against possible misspecification of P 0 , the portfolio selection problem is solved with respect to the worst-case distribution within a Wasserstein-neighborhood of P 0 . We review tractable formulations of the portfolio selection problem under model ambiguity, as it is called in the literature. For instance, it is known that high model ambiguity leads to equally-weighted portfolio diversification. However, it often happens that the marginal distributions of the assets can be estimated with high accuracy, whereas the dependence structure between the assets remains ambiguous. This leads to the problem of portfolio selection under dependence uncertainty. We show that in this case portfolio concentration becomes optimal as the uncertainty with respect to the estimated dependence structure increases. Hence, distributionally robust portfolio optimization can have two very distinct implications: Diversification on the one hand and concentration on the other hand.

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Section: Ambiguity Setsmentioning

confidence: 99%

A Review on Ambiguity in Stochastic Portfolio Optimization

Pflug

Pohl

2017

Set-Valued Var. Anal

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Robust reward–risk ratio portfolio optimization

Sehgal

Mehra

2019

Int Trans Operational Res

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In this paper, we propose robust portfolio optimization models for reward–risk ratios utilizing Omega, semi‐mean absolute deviation ratio, and weighted stable tail adjusted return ratio (STARR). We address the uncertainty in returns on assets by varying them in symmetric bounded intervals. The proposed robust reward–risk ratios preserve linearity in the resulting models, and hence are tractable. However, the robust models involve a sizably voluminous number of constraints, especially when the number of assets and scenarios is large. We employ the cutting plane algorithm to solve the proposed models in a much reduced time efficiently. We evaluate the performance of the robust reward–risk ratio models on the listed stocks of FTSE 100, Nikkei 225, S&P 500, and S&P BSE 500. The robust portfolio optimization models are found to outperform their conventional counterpart models in terms of statistics measured by the standard deviation, value at risk (VaR), conditional value at risk (CVaR), Sharpe ratio, and STARR ratio.

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Data-Driven Distributionally Robust Chance-Constrained Optimization With Wasserstein Metric

Lejeune

2018

SSRN Journal

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Data-Driven Optimization of Ambiguous Reward-Risk Ratio Measures

Cited by 3 publications

References 46 publications

A Review on Ambiguity in Stochastic Portfolio Optimization

A Review on Ambiguity in Stochastic Portfolio Optimization

Robust reward–risk ratio portfolio optimization

Data-Driven Distributionally Robust Chance-Constrained Optimization With Wasserstein Metric

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