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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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TZID:America/Chicago
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TZOFFSETFROM:-0600
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TZNAME:CDT
DTSTART:20260308T080000
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DTSTART:20261101T070000
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DTSTART;TZID=America/Chicago:20260327T120000
DTEND;TZID=America/Chicago:20260327T130000
DTSTAMP:20260412T210830
CREATED:20251215T170856Z
LAST-MODIFIED:20251215T170925Z
UID:2554-1774612800-1774616400@www.math.ttu.edu
SUMMARY:A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
DESCRIPTION:Speaker: Professor Antonis Papapantoleon\, Delft Institute of Applied Mathematics\, EEMCS\, Delft University of Technology \nAbstract: We develop a novel deep learning approach for pricing European options in diffusion models\, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem\, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples\, with particular focus in the lifted Heston model.
URL:https://www.math.ttu.edu/mathematicalfinance/event/a-time-stepping-deep-gradient-flow-method-for-option-pricing-in-rough-diffusion-models/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Papapantoleon-e1765818384977.jpeg
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