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PRODID:-//Mathematical Finance - ECPv5.7.0//NONSGML v1.0//EN
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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
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
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TZNAME:CST
DTSTART:20261101T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260306T140000
DTEND;TZID=America/Chicago:20260306T150000
DTSTAMP:20260410T100923
CREATED:20251202T222739Z
LAST-MODIFIED:20251202T222739Z
UID:2495-1772805600-1772809200@www.math.ttu.edu
SUMMARY:Mean-CVaR portfolio optimization under ESG disagreement
DESCRIPTION:Speaker: Prof. Davide Lauria\, Department of Management\, University of Bergamo \nAbstract: The ESG score of a company is a measure of its commitment to environmental\, social and governance investing standards. ESG scores are produced by rating agencies using unique and proprietary methodologies. The complexity of measurement and the lack of widely accepted standards contribute to inconsistencies across agencies. Discrepancies in ratings issued by multiple data providers are particularly relevant in portfolio optimization problems that integrate ESG objectives into the classical risk-reward framework. In this work\, we specifically study the impact on portfolio composition by examining Mean-CVaR-ESG optimal portfolios\, where the objective function incorporates the portfolio’s ESG score. To address ESG score discrepancies\, we introduce a Distributionally Robust Optimization (DRO) reformulation of the Mean-CVaR-ESG model and assess its potential benefits. Our findings reveal a persistent divergence in optimal strategies across the investment horizon when ESG values from different rating agencies are used. We then apply the DRO approach by replacing a single provider’s ESG score with a statistic derived from the scores of five different agencies. Our results show that\, in this case\, the DRO approach effectively mitigates score discrepancies by significantly reducing optimal portfolio concentration while enhancing the ESG evaluation of optimal portfolios across all rating agencies.
URL:https://www.math.ttu.edu/mathematicalfinance/event/mean-cvar-portfolio-optimization-under-esg-disagreement/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2021/06/Screen-Shot-2021-06-29-at-10.17.32-PM-e1764695697225.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260313T140000
DTEND;TZID=America/Chicago:20260313T150000
DTSTAMP:20260410T100923
CREATED:20251201T181134Z
LAST-MODIFIED:20251201T181158Z
UID:2482-1773410400-1773414000@www.math.ttu.edu
SUMMARY:Portfolio optimization in a market with hidden Gaussian drift and expert opinions
DESCRIPTION:Speaker: Prof. Ralf Wunderlich\, Institute of Mathematics\, Brandenburg University of Technology Cottbus-Sentenberg\, Germany \nAbstract: This paper investigates the optimal selection of portfolios for power utility maximizing investors in a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at fixed and known dates or randomly over time.  Applying Kalman filter techniques we derive estimates of the hidden drift which are described by the conditional mean and covariance of the drift given the observations. The utility maximization problem is solved with dynamic programming methods. \nFor expert opinions that arrive on fixed dates\, the corresponding dynamic programming equation (DPE) can be solved in closed form\, and the value function and\nthe optimal trading strategy for an investor can be derived. They make it possible to quantify the monetary value of the information provided by the expert opinions. We illustrate our theoretical findings with results from numerical experiments. \nIf the arrival dates are random and modeled as the jump times of a homogeneous Poisson process\, the DPE is a partial integro-differential equation and degenerate in the diffusion part of the differential operator. We therefore adopt a regularization approach and add a Brownian perturbation to the state process\, scaled by a small parameter that approaches zero. We prove that the value functions of the regularized problems converge to the value function of the original problem. This enables the construction of ε-optimal strategies.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-optimization-in-a-market-with-hidden-gaussian-drift-and-expert-opinions/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Wunderlich-e1764612535510.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260320T140000
DTEND;TZID=America/Chicago:20260320T150000
DTSTAMP:20260410T100923
CREATED:20251124T233111Z
LAST-MODIFIED:20251124T233212Z
UID:2451-1774015200-1774018800@www.math.ttu.edu
SUMMARY:No Seminar - Spring Break
DESCRIPTION:
URL:https://www.math.ttu.edu/mathematicalfinance/event/no-seminar-thanksgiving-break-2/
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/unhappy.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260327T120000
DTEND;TZID=America/Chicago:20260327T130000
DTSTAMP:20260410T100923
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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