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UID:20260601T205819EDT-8317h3GHdc@132.216.98.100
DTSTAMP:20260602T005819Z
DESCRIPTION:Title: Risk assessment\, heavy tails\, and asymmetric least squ
 ares techniques\n\nAbstract:\n\nStatistical risk assessment\, in particula
 r in finance and insurance\, requires estimating simple indicators to summ
 arize the risk incurred in a given situation. Of most interest is to infer
  extreme levels of risk so as to be able to manage high-impact rare events
  such as extreme climate episodes or stock market crashes. A standard proc
 edure in this context\, whether in the academic\, industrial or regulatory
  circles\, is to estimate a well-chosen single quantile (or Value-at-Risk)
 . One drawback of quantiles is that they only take into account the freque
 ncy of an extreme event\, and in particular do not give an idea of what th
 e typical magnitude of such an event would be. Another issue is that they 
 do not induce a coherent risk measure\, which is a serious concern in actu
 arial and financial applications. In this talk\, after giving a leisurely 
 tour of extreme quantile estimation\, I will explain how\, starting from t
 he formulation of a quantile as the solution of an optimization problem\, 
 one may come up with two alternative families of risk measures\, called ex
 pectiles and extremiles\, in order to address these two drawbacks. I will 
 give a broad overview of their properties\, as well as of their estimation
  at extreme levels in heavy-tailed models\, and explain why they constitut
 e sensible alternatives for risk assessment using real data applications. 
 This is based on joint work with Abdelaati Daouia\, Irène Gijbels\, Stépha
 ne Girard\, Simone Padoan and Antoine Usseglio-Carleve.\n\nhttps://umontre
 al.zoom.us/j/93983313215?pwd=clB6cUNsSjAvRmFMME1PblhkTUtsQT09\n\nMeeting I
 D: 939 8331 3215\n\nPasscode: 096952\n\n\n	\n		 \n			 \n		\n	\n\n
DTSTART:20220128T203000Z
DTEND:20220128T213000Z
SUMMARY:Gilles Stupfler (ENSAI)
URL:/mathstat/channels/event/gilles-stupfler-ensai-337
 149
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