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SUMMARY:Machine Learning for Volatility Trading
DTSTART:20180320T120000
DTEND:20180320T130000
DTSTAMP:20260407T195436Z
UID:29e231a1d0a00223e4a93ecff2116472a57d4696820f49965c1905d1
CATEGORIES:Conferences - Seminars
DESCRIPTION:Artur SEPP (Julius Baer)\nMany applications of quantitative tr
 ading and investing require the forecast of the future realized volatility
  as the fundamental input. While there are many models for volatility meas
 urement and forecast\, the key decision is how to select the best models w
 ith the highest predicative power for a given application. I apply the met
 hods of supervised machine learning and learning to rank for the machine-b
 ased selection of volatility models. I demonstrate applications of this fr
 amework to volatility trading and risk management of option books.
LOCATION:UNIL\, Extranef\, room 126 https://planete.unil.ch/plan/?local=EX
 T-126
STATUS:CONFIRMED
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