BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260129T110000
DTEND;TZID=Europe/Helsinki:20260129T120000
DTSTAMP:20260709T214312
CREATED:20260121T140743Z
LAST-MODIFIED:20260123T101240Z
UID:18751-1769684400-1769688000@crest.science
SUMMARY:Guillermo ALONSO-ALVAREZ ( University of Michigan) "Contracting a crowd of heterogeneous agents"
DESCRIPTION:Finance-Insurance\nTime: 11.00 am\nDate : 29Th of January 2025\nRoom 3001 \nGuillermo ALONSO-ALVAREZ ( University of Michigan) “Contracting a crowd of heterogeneous agents” \nAbstract : In incomplete financial markets\, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. We introduce a constrained deep learning framework to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the option price function\, with its gradient serving as the hedging strategy\, optimized via a loss function that enforces the self-financing portfolio condition. A major difficulty stems from the non-smooth nature of option payoffs (e.g.\, vanilla calls are non-differentiable at-the-money\, digital options are discontinuous)\, which conflicts with the intrinsic smoothness of standard neural networks. To overcome this\, we compare unconstrained architectures with constrained networks that explicitly incorporate the terminal payoff condition\, drawing inspiration from PDE boundary embedding techniques. We further explore an extension of this framework by integrating Howard’s policy iteration algorithm within a reinforcement learning perspective. This direction aims to leverage the efficiency of policy iteration while preserving the terminal payoff constraint through the constrained network architecture. \n  \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/guillermo-alonso-alvarez-university-of-michigan-contracting-a-crowd-of-heterogeneous-agents/
CATEGORIES:Finance-Insurance,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR