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UID:20251210T210515EST-5163oMRGgo@132.216.98.100
DTSTAMP:20251211T020515Z
DESCRIPTION:Title: Robust Distortion Risk Measures\n\nAbstract: In the pres
 ence of uncertainty\, robustness of risk measures is of crucial importance
 . Distributional uncertainty may be accounted for by providing bounds on t
 he values of a risk measure\, so-called worst- and best-case risk measures
 . Worst (best)-case risk measures are determined as the maximal (minimal) 
 value a risk measure can attain when the underlying distribution is unknow
 n - usually up to its first moments. However\, these bounds as well as the
  (worst- and best-case) distributions that attain the worst- and best-case
  values are too large\, respectively “unrealistic”\, to be practically rel
 evant.\n	\n	We provide sharp bounds for the class of distortion risk measure
 s with constraints on the first two moments combined with a constraint on 
 the Wasserstein distance with respect to a reference distribution. Adding 
 the Wasserstein distance constraint\, leads to significantly improved boun
 ds and more “realistic” worst-case distributions. Specifically\, the worst
 -case distribution of the two most widely used risk measures\, the Value-a
 t-Risk and the Tail-Value-at-Risk\, depend on the reference distribution a
 nd thus\, are no longer two-point distributions.\n
DTSTART:20191114T184500Z
DTEND:20191114T184500Z
LOCATION:Room LB 921-4\, CA\, Concordia University
SUMMARY:Silvana Pesenti  (University of Toronto)
URL:/mathstat/channels/event/silvana-pesenti-universit
 y-toronto-302632
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