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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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DTSTART:20250309T080000
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DTSTART;TZID=America/Chicago:20250926T120000
DTEND;TZID=America/Chicago:20250926T130000
DTSTAMP:20260411T003005
CREATED:20250707T130236Z
LAST-MODIFIED:20250707T184623Z
UID:2048-1758888000-1758891600@www.math.ttu.edu
SUMMARY:Risk-aware Trading Portfolio Optimization
DESCRIPTION:Speakers: Dr. Marco Bianchetti\, Head of Market and Counterparty Risk IMA Methodologies\, Intesa Sanpaolo\, Milan\, Italy\n               Dr Fabio Vitale\, Senior Researcher\, CENTAI Institute\, Turin\, Italy \nAbstract: We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO)\, formulate the corresponding optimization problem\, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P\, a specific set of Unique Eligible Instruments (UEIs)\, their combination into an Eligible Optimization Strategy (EOS)\, an objective function\, and a set of constraints. RATS searches for an optimal EOS that\, added to P\, improves the objective function respecting the constraints. RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs\, enables parallel computation with a large number of particles\, and is fully general with respect to specific choices of the key elements\, which can be customized to encode financial knowledge and needs of traders and risk managers. We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments\, an objective function combining both market risk (VaR) and profit & loss measures\, constraints on market sensitivities and UEIs trading costs. In the case of small-sized EOS\, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameter tuning. In the case of large-sized EOS\, RATS markedly improves the portfolio objective value\, optimizing risk and capital charge while respecting risk limits and preserving expected profits. Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements\, allowing a better alignment of business and risk management objectives.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-6/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Bianchetti_Vitale.jpg
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