Robust risk management

Fertis, Apostolos G.; Baes, Michel; Lüthi, Hans-Jakob

doi:10.1016/j.ejor.2012.03.036

Cited by 34 publications

(18 citation statements)

References 23 publications

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“…In particular, the significance of minimum risk portfolios has been questioned when studying the problem of optimal asset allocation: several authors (among them El Ghaoui et al 2003, Fertis et al 2012, Zymler et al 2013) have recently considered this issue from a robust optimization perspective.…”

Section: Introductionmentioning

confidence: 99%

Assessing Financial Model Risk

Barrieu

Scandolo

2013

SSRN Journal

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Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.

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Section: Introductionmentioning

confidence: 99%

Assessing Financial Model Risk

Barrieu

Scandolo

2013

SSRN Journal

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“…However, in many settings, the random variables considered are either an affine or linear function of a, potentially correlated, vector of random parameters. A classical example is portfolio optimization (see for example Markowitz (1952); Konno and Yamazaki (1991); Black and Litterman (1992); Cvitanić and Karatzas (1992); Krokhmal et al (2002); Zymler et al (2011);Lim et al (2011); Kawas and Thiele (2011);Fertis et al (2012); Kolm et al (2014)) where the random return of a portfolio is usually modeled as a weighted linear combination of the random returns of individual assets (with weights equal to the fraction invested in a given asset) plus a possibly null constant representing investment in a riskless asset. In this paper we show that imposing the coherence and distortion axioms only on random variables that are a linear, or affine linear function of a vector of random variables allows the inclusion of uncertainty sets that are deemed invalid by the classical characterizations.…”

Section: Introductionmentioning

confidence: 99%

Restricted risk measures and robust optimization

Lagos

Espinoza

Moreno

et al. 2015

European Journal of Operational Research

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In this paper we consider characterizations of the robust uncertainty sets associated with coherent and distortion risk measures. In this context we show that if we are willing to enforce the coherent or distortion axioms only on random variables that are affine or linear functions of the vector of random parameters, we may consider some new variants of the uncertainty sets determined by the classical characterizations. We also show that in the finite probability case these variants are simple transformations of the classical sets. Finally we present results of computational experiments that suggest that the risk measures associated with these new uncertainty sets can help mitigate estimation errors of the Conditional Value-at-Risk.

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“…The scenario set is constructed by structuring randomness in two stages and concentrating uncertainty at the first stage. Again, dualizing the representation enables portfolio optimization using Robust CVaR [11,25].…”

Section: Introductionmentioning

confidence: 99%

“…Portfolio optimization using CVaR is implemented by dualizing its representation via the scenario set [23]. When the probability distribution is unknown but can be restricted to a scenario set containing all the potential distributions, Robust Conditional Value-at-Risk (Robust CVaR) can be defined as the worst-case CVaR when the distribution varies in this set [11,25]. The scenario set is constructed by structuring randomness in two stages and concentrating uncertainty at the first stage.…”

Section: Introductionmentioning

confidence: 99%

The Regularization Aspect of Optimal-Robust Conditional Value-at-Risk Portfolios

Fertis

Baes²,

Lüthi³

2012

Operations Research Proceedings

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In portfolio management, Robust Conditional Value -at -Risk (Robust CVaR) has been proposed to deal with structured uncertainty in the estimation of the assets probability distribution. Meanwhile, regularization in portfolio optimization has been investigated as a way to construct portfolios that show satisfactory out-ofsample performance under estimation error. In this paper, we prove that optimalRobust CVaR portfolios possess the regularization property. Based on expected utility theory concepts, we explicitly derive the regularization scheme that these portfolios follow and its connection with the scenario set properties.

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Robust risk management

Cited by 34 publications

References 23 publications

Assessing Financial Model Risk

Assessing Financial Model Risk

Restricted risk measures and robust optimization

The Regularization Aspect of Optimal-Robust Conditional Value-at-Risk Portfolios

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