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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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TZOFFSETFROM:-0600
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TZNAME:CDT
DTSTART:20260308T080000
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DTSTART:20261101T070000
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DTSTART;TZID=America/Chicago:20260918T140000
DTEND;TZID=America/Chicago:20260918T150000
DTSTAMP:20260907T180553
CREATED:20260825T141701Z
LAST-MODIFIED:20260825T141701Z
UID:3243-1789740000-1789743600@www.math.ttu.edu
SUMMARY:Seminar: Optimal Fund Menus
DESCRIPTION:  \nSpeaker:  Jakša Cvitanić\, Caltech Humanities and Social Sciences \nAbstract: We study the optimal design of a menu of funds by a manager who is required to use linear pricing and does not observe the beliefs of investors regarding one of the risky assets. The optimal menu involves bundling of assets and can be constructed from the solution to a calculus of variations problem that optimizes over the indirect utility that each type receives. We provide a complete characterization of the optimal menu and show that the need to maintain incentive compatibility leads the manager to offer funds that are inefficiently tilted towards the asset that is not subject to the information friction. \n  \nJoin Zoom Meeting \nhttps://texastech.zoom.us/j/3067000354?pwd=S0nCdfz1Ue6kOR9aBFgx67IEXPuNZd.1&omn=92056236741 \n  \nMeeting ID: 306 700 0354 \nPasscode: TTUMF \n  \n  \n 
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-optimal-fund-menus/
LOCATION:via Zoom
CATEGORIES:Fall 2026
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DTSTART;TZID=America/Chicago:20260925T110000
DTEND;TZID=America/Chicago:20260925T120000
DTSTAMP:20260907T180553
CREATED:20260719T142839Z
LAST-MODIFIED:20260719T142839Z
UID:3055-1790334000-1790337600@www.math.ttu.edu
SUMMARY:Seminar: Challenges in Using Machine Learning for Rough Volatility Models
DESCRIPTION:Speaker: Dan Leonte\, Postdoctoral Research Fellow\, KAUST \nAbstract: Since the seminal work of Gatheral\, Jaisson\, and Rosenbaum (2014)\, it has become widely accepted that volatility in derivatives markets exhibits rough behavior. Numerical methods for rough volatility models are often difficult to stabilize and scale to large volatility smile surfaces. In addition\, calibration to volatility surfaces\, rather than to historical data\, remains a significant challenge\, as does the reliable valuation of exotic derivatives. In this work\, we clarify how machine learning tools can be used most effectively to address these problems. \nHongwei Mei is inviting you to a scheduled Zoom meeting. \nTopic: Mathematical Finance Seminar\nTime: Sep 25\, 2026 11:00 AM Central Time (US and Canada)\nJoin Zoom Meeting\nhttps://texastech.zoom.us/j/3067000354?pwd=S0nCdfz1Ue6kOR9aBFgx67IEXPuNZd.1&omn=93879608710 \nMeeting ID: 306 700 0354\nPasscode: TTUMF \n— \nOne tap mobile\n+13462487799\,\,3067000354#\,\,\,\,*264811# US (Houston)\n+12532158782\,\,3067000354#\,\,\,\,*264811# US (Tacoma) \n— \nJoin by SIP\n• 3067000354@zoomcrc.com \nJoin instructions\nhttps://texastech.zoom.us/meetings/93879608710/invitations?signature=tm7NJDEtgFLn7uQIM5eIKyJ9mPuS2XzlTzowMICmmuo \n  \n 
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-challenges-in-using-machine-learning-for-rough-volatility-models/
LOCATION:via Zoom
CATEGORIES:Fall 2026
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