77 citations · 80 across the 10 of their papers we have counts for
11 papers
Hidden Dependence and Aggregate Tail Risk
Corrado De Vecchi, Max Nendel, Steven Vanduffel
We study risk aggregation problems for arbitrary non-decreasing aggregation functions and tail risk measures under dependence uncertainty in a distributionally robust setting. To t…
Robust distortion risk metrics with Wasserstein and Unimodal Constraints
Peng Liu, Steven Vanduffel, Yi Xia +1
We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlyin…
Higher moments under dependence uncertainty with applications in insurance
Carole Bernard, Jinghui Chen, Steven Vanduffel
Recent studies have highlighted the significance of higher-order moments - such as coskewness - in portfolio optimization within the financial domain. This paper extends that focus…
Modeling coskewness with zero correlation and correlation with zero coskewness
Carole Bernard, Jinghui Chen, Steven Vanduffel
This paper shows that one needs to be careful when making statements on potential links between correlation and coskewness. Specifically, we first show that, on the one hand, it is…
Coskewness under dependence uncertainty
Carole Bernard, Jinghui Chen, Ludger Ruschendorf +1
We study the impact of dependence uncertainty on the expectation of the product of random variables, when for all~. Under some…
Cost-efficient Payoffs under Model Ambiguity
Carole Bernard, Gero Junike, Thibaut Lux +1
Dybvig (1988a,b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution ("cost-efficient payoff"). In…
Robust Distortion Risk Measures
Carole Bernard, Silvana M. Pesenti, Steven Vanduffel
The robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance in making well-informed decisions. In this paper,…
The Minimum Regularized Covariance Determinant estimator
Kris Boudt, Peter J. Rousseeuw, Steven Vanduffel +1
The Minimum Covariance Determinant (MCD) approach robustly estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its m…
Quantile of a Mixture
Carole Bernard, Steven Vanduffel
In this note, we give an explicit expression for the quantile of a mixture of two random variables. We carefully examine all possible cases of discrete and continuous variables wit…
Optimal Payoffs under State-dependent Preferences
Carole Bernard, Franck Moraux, Ludger Rueschendorf +1
Most decision theories, including expected utility theory, rank dependent utility theory and cumulative prospect theory, assume that investors are only interested in the distributi…
Rationalizing Investors Choice
Carole Bernard, Jit Seng Chen, Steven Vanduffel
Assuming that agents' preferences satisfy first-order stochastic dominance, we show how the Expected Utility paradigm can rationalize all optimal investment choices: the optimal in…