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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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DTSTART:20260308T080000
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DTSTART:20261101T070000
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DTSTART;TZID=America/Chicago:20260424T090000
DTEND;TZID=America/Chicago:20260424T100000
DTSTAMP:20260521T202559
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
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DTSTART;TZID=America/Chicago:20260501T140000
DTEND;TZID=America/Chicago:20260501T150000
DTSTAMP:20260521T202559
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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