"We study the general properties of robust convex risk measures as worst-case values under uncertainty on random variables. We establish general concrete results regarding convex conjugates and sub-differentials. We refine some results for closed forms of worstcase law invariant convex risk measures under two concrete cases of uncertainty sets for random variables: based on the first two moments and Wasserstein balls."
top of page
Rechercher
Posts récents
Voir tout“As analysts are primary recipients of these reports, we investigate whether and how analyst forecast properties have changed following...
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest...
Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers...
bottom of page
Comments