Extremal Dependence Concepts

Puccetti, Giovanni; Wang, Ruodu

doi:10.1214/15-sts525

Cited by 106 publications

(77 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, these bounds have been extensively studied in recent years and partial solutions have been obtained, see Puccetti and Wang [46] for an overview. Most importantly, Puccetti and Rüschendorf [45] introduced the so-called rearrangement algorithm which is a fast procedure to numerically compute the bounds of interest.…”

Section: Introductionmentioning

confidence: 99%

A Review on Ambiguity in Stochastic Portfolio Optimization

Pflug

Pohl

2017

Set-Valued Var. Anal

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

A Review on Ambiguity in Stochastic Portfolio Optimization

Pflug

Pohl

2017

Set-Valued Var. Anal

View full text Add to dashboard Cite

show abstract

“…A simple proof without the assumption that the underlying probability space (Ω, F , P ) is atomless was given by Mao and Hu [18]. Some equivalent conditions on comonotonicity can be found in [19]. To summarize the above results, we arrive at the following theorem:…”

Section: Introductionmentioning

confidence: 84%

Sharp Convex Bounds on the Aggregate Sums–An Alternative Proof

Yin

Zhu

2016

Risks

View full text Add to dashboard Cite

Abstract:It is well known that a random vector with given marginals is comonotonic if and only if it has the largest convex sum, and that a random vector with given marginals (under an additional condition) is mutually exclusive if and only if it has the minimal convex sum. This paper provides an alternative proof of these two results using the theories of distortion risk measure and expected utility.

show abstract

“…More details about comonotonicity and mutual exclusivity can be found in Dhaene and Denuit [10], Cheung and Lo [15,16], Mesfioui and Denuit [17], and Puccetti and Wang [18]. …”

Section: Lemma 8 For Any Random Vectormentioning

confidence: 99%