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SUMMARY:Machine Learning for Continuous-Time Finance
DTSTART:20200214T103000
DTEND:20200214T120000
DTSTAMP:20260406T111825Z
UID:74af6ff40c749940928fb7cc5992a8055f58dc26558fe0ba6cd927f3
CATEGORIES:Conferences - Seminars
DESCRIPTION:Victor DUARTE\, Gies College of Business - UIUC\nThis paper pr
 oposes an algorithm for solving a large class of nonlinear continuous-time
  models in finance and economics. First\, I recast the problem of solving 
 the corresponding nonlinear partial differential equations as a sequence o
 f supervised learning problems. Second\, I prove that the computational co
 st of evaluating the exact continuous-time Bellman residuals does not incr
 ease in the number of state variables\, allowing for the solution of high-
 dimensional problems. To illustrate the method\, I solve canonical asset p
 ricing models featuring recursive preferences\, endogenous labor supply\, 
 irreversible investment\, and state spaces with up to ten dimensions.\n 
LOCATION:UNIL\, Extranef\, room 126
STATUS:CONFIRMED
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