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VERSION:2.0
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
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
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TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260306T140000
DTEND;TZID=America/Chicago:20260306T150000
DTSTAMP:20260430T002602
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260313T140000
DTEND;TZID=America/Chicago:20260313T150000
DTSTAMP:20260430T002602
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260320T140000
DTEND;TZID=America/Chicago:20260320T150000
DTSTAMP:20260430T002602
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:20260430T002602
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260403T120000
DTEND;TZID=America/Chicago:20260403T130000
DTSTAMP:20260430T002602
CREATED:20251121T173246Z
LAST-MODIFIED:20251121T173854Z
UID:2430-1775217600-1775221200@www.math.ttu.edu
SUMMARY:ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Endogenous Price Formation and Execution in a Limit Order Book
DESCRIPTION:Speaker: Dr. Jean-Loup Dupret\, Department of Mathematics\, ETH Zurich \nAbstract: We present ABIDES-MARL\, a framework that combines a new multi-agent reinforcement learning (MARL) methodology with a new realistic limit-order-book (LOB) simulation system to study equilibrium behavior in complex financial market games. The system extends ABIDES-Gym by decoupling state collection from kernel interruption\, enabling synchronized learning and decision-making for multiple adaptive agents while maintaining compatibility with standard RL libraries. It preserves key market features such as price–time priority and discrete tick sizes. Methodologically\, we use MARL to approximate equilibrium-like behavior in multi-period trading games with a finite number of heterogeneous agents—an informed trader\, a liquidity trader\, noise traders\, and competing market makers—all with individual price impacts. This setting bridges optimal execution and market microstructure by embed ding the liquidity trader’s optimization problem within a strategic trading environment. We validate the approach by solving an extended Kyle model within the simulation system\, recovering the gradual price discovery phenomenon. We then extend the analysis to a liquidity trader’s problem where market liquidity arises endogenously and show that\, at equilibrium\, execution strategies shape market-maker behavior and price dynamics. ABIDES-MARL provides a reproducible foundation for analyzing equilibrium and strategic adaptation in realistic markets and contributes toward building economically interpretable agentic AI systems for finance.
URL:https://www.math.ttu.edu/mathematicalfinance/event/abides-marl-a-multi-agent-reinforcement-learning-environment-for-endogenous-price-formation-and-execution-in-a-limit-order-book/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/11/Dupret-e1763746714889.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260417T140000
DTEND;TZID=America/Chicago:20260417T150000
DTSTAMP:20260430T002602
CREATED:20251210T203233Z
LAST-MODIFIED:20260109T214820Z
UID:2528-1776434400-1776438000@www.math.ttu.edu
SUMMARY:Corruption via Mean Field Games
DESCRIPTION:Speaker: Dr. Kirill Golubnichiy\, Department of Mathematics & Statistics\, Texas Tech University \nAbstract: A new mathematical model describing the evolution of a corrupted hierarchy is derived. This model is based on mean field games theory. We consider a retrospective (inverse) problem for this model. From an applied standpoint\, this problem amounts to reconstructing the past activity of the corrupted hierarchy using present-time data for this community. We derive three new Carleman estimates. These estimates yield Hölder stability and uniqueness results for both the retrospective problem and its generalized version. The Hölder stability estimates characterize how the error in the solution of the retrospective problem depends on the error in the input data.
URL:https://www.math.ttu.edu/mathematicalfinance/event/corruption-via-mean-field-games/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/Golubnichiy.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260424T090000
DTEND;TZID=America/Chicago:20260424T100000
DTSTAMP:20260430T002602
CREATED:20260311T203054Z
LAST-MODIFIED:20260421T153933Z
UID:2826-1777021200-1777024800@www.math.ttu.edu
SUMMARY:Dynamic asymmetric tail dependence structure among multi-asset classes for portfolio management: Dynamic skew- copula approach
DESCRIPTION:Speaker: Dr. Kakeru Ito\, Senior Portfolio Manager (Multi-Asset / Quants)\, Mizuho Securities Co.\, Ltd.\nand Visiting Researcher\, Graduate School of Management\, Tokyo Metropolitan University  \nAbstract:  This study proposes AC dynamic skew-𝑡 copula with cDCC model to capture the dynamic asymmetric tail dependence structure among multi-asset classes (government bonds\, corporate bonds\, equities\, and REITs). We provide new evidence that lower tail dependence coefficients increased compared to upper ones for all pairs in the COVID-19 crash and the recent high inflation period\, indicating that the diversification effect through multi-asset investment decreased. Our empirical analysis also shows that in terms of AIC and BIC\, dynamic AC skew-𝑡 copula fits data of multi-asset classes better than other dynamic elliptical copulas because it can consider the above dependence structure characteristics. Furthermore\, out-of-sample analysis reveals that considering an asymmetry of tail dependence structure at each point with an AC dynamic skew-𝑡 copula enhances expected shortfall (ES) estimation accuracy and the performance of a minimum ES portfolio. These results indicate that capturing dynamic asymmetric tail dependence is crucial for multi-asset portfolio management.
URL:https://www.math.ttu.edu/mathematicalfinance/event/dynamic-asymmetric-tail-dependence-structure-among-multi-asset-classes-for-portfolio-management-dynamic-skew-copula-approach/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2026/04/ito-e1776785927134.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260501T140000
DTEND;TZID=America/Chicago:20260501T150000
DTSTAMP:20260430T002602
CREATED:20251229T180758Z
LAST-MODIFIED:20251229T180758Z
UID:2595-1777644000-1777647600@www.math.ttu.edu
SUMMARY:Differential Beliefs in Financial Markets Under Information Constraints: A Modeling Perspective
DESCRIPTION:Speaker: Prof. Karen Grigorian\, Department of Statistics and Applied Probability\, UC Santa Barbara \nAbstract: We apply the theory of McKean-Vlasov-type SDEs to study several problems related to market efficiency in the context of partial information and partially observable financial markets: (i) convergence of reduced-information market price processes to the true price process under an increasing information flow; (ii) a specific mechanism of shrinking biases under increasing information flows; (iii) optimal aggregation of expert opinions by a trader seeking a positive alpha. All these problems are studied by means of (conditional) McKean-Vlasov-type SDEs\, Wasserstein barycenters\, KL divergence and relevant tools from convex optimization\, optimal control and nonlinear filtering. We supply the theoretical results in (i)-(iii) with concrete simulations demonstrating how the proposed models can be applied in practice to model financial markets under information constraints and the arbitrage-seeking behavior of traders with differential beliefs.
URL:https://www.math.ttu.edu/mathematicalfinance/event/differential-beliefs-in-financial-markets-under-information-constraints-a-modeling-perspective/
LOCATION:via Zoom
CATEGORIES:Spring 2026
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