Most often, important decisions involve several unknown attributes. This produces a double
challenge in the sense that both assessing the individual multiattribute preferences and …

This paper presents a widely applicable approach to solving (multi-marginal, martingale)
optimal transport and related problems via neural networks. The core idea is to penalize the …

Quantile aggregation with dependence uncertainty has a long history in probability theory,
with wide applications in finance, risk management, statistics, and operations research …

We consider derivatives written on multiple underlyings in a one-period financial market,
and we are interested in the computation of model-free upper and lower bounds for their …

Distributionally robust optimization (DRO) incorporates robustness against uncertainty in the
specification of probabilistic models. This paper focuses on mitigating the curse of …

We show how inter-asset dependence information derived from market prices of options can
lead to improved model-free price bounds for multi-asset derivatives. Depending on the type …

The problem of the Dedekind-MacNeille completion of the class of n–copulas originated in
2005 and is well-known to all the experts in the field. Unlike in the bivariate case, where the …

We derive bounds on the distribution function, therefore also on the Value-at-Risk, of φ (X)
where φ is an aggregation function and X=(X 1,…, X d) is a random vector with known …

We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional)
distribution classes in which some variables are continuous-valued. We establish the …

Abstract Value‐at‐Risk (VaR) bounds for aggregated risks have been derived in the
literature in settings where, besides the marginal distributions of the individual risk factors …