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PRODID:-//Mathematical Finance - ECPv5.7.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
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TZNAME:CST
DTSTART:20241103T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241104T120000
DTEND;TZID=America/Chicago:20241104T130000
DTSTAMP:20260414T131115
CREATED:20240729T184309Z
LAST-MODIFIED:20240729T195746Z
UID:1491-1730721600-1730725200@www.math.ttu.edu
SUMMARY:Pricing options with a new hybrid neural network model
DESCRIPTION:Speaker: Dr. Yossi Shvimer\, Research Associate\, School of Finance and Management\, SOAS University of London \nAbstract: A novel hybrid option pricing model using a deep learning neural network has been developed. The hybrid model keeps the traditional option pricing model with the same input parameters while simultaneously adjusting the model with neural network methods to improve accuracy when applied to real market data\, especially in OTM options. The new hybrid model demonstrates superior accuracy compared to both traditional parametric and non-parametric option pricing models for both Call and Put options across all moneyness levels. The empirical results of the hybrid model provide an explanation for the deviation from the Put-Call parity observed in real market data.
URL:https://www.math.ttu.edu/mathematicalfinance/event/pricing-options-with-a-new-hybrid-neural-network-model/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/07/Yossi_Shvimer.png
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