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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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DTSTART:20240310T080000
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DTSTART:20241103T070000
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DTSTART;TZID=America/Chicago:20241104T120000
DTEND;TZID=America/Chicago:20241104T130000
DTSTAMP:20241114T141725
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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DTSTART;TZID=America/Chicago:20241115T140000
DTEND;TZID=America/Chicago:20241115T150000
DTSTAMP:20241114T141725
CREATED:20240729T184547Z
LAST-MODIFIED:20240816T214112Z
UID:1493-1731679200-1731682800@www.math.ttu.edu
SUMMARY:Stochastic dominance\, stochastic volatility\, and jump risk: new theory interprets old results
DESCRIPTION:Speaker: Prof Stylianos Perrakis\, John Molson School of Business\, Concordia Univ\, Montreal \n Abstract: The stochastic dominance (SD) approach is applied to the valuation of index options in frictionless markets for a wide class of stochastic volatility (SV) processes. SD allows for the derivation of a unique\, exponential option pricing kernel based on the physical underlying return and volatility dynamics. A lower bound and an upper bound on option prices are obtained\, for a wide class of stochastic volatility jump (SVJ) processes that feature jumps in addition to diffusion. Using parameter estimates for the physical process from high-profile studies\, the bounds are shown to be remarkably tight\, especially for the empirically important class of short-term near-the-money options. The bounds are in many cases inconsistent with separate parameter estimates for the risk-neutral process that are extracted from observed option prices: for many option series\, the risk-neutral value exceeds the SD upper bound. This inconsistency points at the possibility that the distributional shape of the risk-neutral process is mis-specified or that the parameters are estimated without properly taking the option bid-ask spread into account.
URL:https://www.math.ttu.edu/mathematicalfinance/event/stochastic-dominance-stochastic-volatility-and-jump-risk-new-theory-interprets-old-results/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/07/perrakis.jpg
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DTSTART;TZID=America/Chicago:20241122T140000
DTEND;TZID=America/Chicago:20241122T150000
DTSTAMP:20241114T141725
CREATED:20240430T212747Z
LAST-MODIFIED:20240903T172510Z
UID:1379-1732284000-1732287600@www.math.ttu.edu
SUMMARY:Inverse Problem for Forecasting Stock Options Prices
DESCRIPTION:Speaker: Dr. Kirill Golubnichiy\, Dept of Math & Statistics\, Texas Tech University \nAbstract: We present a new heuristic mathematical model for accurate forecasting of prices of stock options for 1-2 trading days ahead of the present one. This new technique uses the Black-Scholes equation supplied by new intervals for the underlying stock and new initial and boundary conditions for option prices. The Black-Scholes equation was solved in the positive direction of the time variable\, This ill-posed initial boundary value problem was solved by the so-called Quasi-Reversibility Method (QRM). This approach with an added trading strategy was tested on the market data for 368 stock options and good forecasting results were demonstrated. In the current paper\, we use the geometric Brownian motion to provide an explanation of that effectivity using computationally simulated data for European call options. We also provide a convergence analysis for QRM. The key tool of that analysis is a Carleman estimate.
URL:https://www.math.ttu.edu/mathematicalfinance/event/inverse-problem-for-forecasting-stock-options-prices/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/Golubnichiy.jpeg
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