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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
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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:20260403T120000
DTEND;TZID=America/Chicago:20260403T130000
DTSTAMP:20260411T061514
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
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DTSTART;TZID=America/Chicago:20260417T140000
DTEND;TZID=America/Chicago:20260417T150000
DTSTAMP:20260411T061514
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
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260424T090000
DTEND;TZID=America/Chicago:20260424T100000
DTSTAMP:20260411T061514
CREATED:20260311T203054Z
LAST-MODIFIED:20260323T173753Z
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 \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
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