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X-WR-CALDESC:Events for CREST
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260120T140000
DTEND;TZID=Europe/Helsinki:20260120T151500
DTSTAMP:20260825T173832
CREATED:20251229T140040Z
LAST-MODIFIED:20260102T075311Z
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SUMMARY:Màxim SANDIUMENGE-I-BOY (Toulouse School of Economics) "Consumer Dynamics and Vertical Relations: Coordination and Foreclosure in a U.S. Consumer-Goods Industry”"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15- 13h30\nDate : 20 th  January 2026 \nSalle 3001 \nMàxim SANDIUMENGE-I-BOY (Toulouse School of Economics) “Consumer Dynamics and Vertical Relations: Coordination and Foreclosure in a U.S. Consumer-Goods Industry” \nAbstract: Vertical mergers along the supply chain can generate efficiency gains by improving coordination\, and anti-competitive harms by disadvantaging rivals. However\, they are typically analyzed in a static setting due to their complexity\, limiting our ability to assess recent policy concerns about their dynamic consequences. This paper evaluates how demand-induced dynamics reshape the effects of vertical mergers and uses deep reinforcement learning to overcome the associated computational challenges. To do so\, I develop a dynamic model in which downstream firms have multiple suppliers and face dynamic demand\, and I train neural networks to approximate the Markov perfect equilibrium. I estimate the model using data from a U.S. consumer-goods industry\, where habit formation in demand induces strong dynamics. The findings reveal that dynamic considerations magnify the consequences of any competitive disadvantage. This prompts firms to moderate their margins\, but also strengthens their incentives to disadvantage non-integrated suppliers. Together\, these forces produce a perverse outcome: as demand dynamics strengthen\, efficiency gains from integration shrink by up to 35% compared to the static case\, and integrated firms reduce prices of integrated products up to 30% less\, while foreclosing non-integrated products more severely. Overall\, intertemporal linkages dampen the pro-competitive effects of vertical mergers and amplify their anti-competitive risks.  \nOrganizer : Marie-Laure ALLAIN \n  \n  \n
URL:https://crest.science/event/maxim-sandiumenge-i-boy-toulouse-school-of-economics-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260120T140000
DTEND;TZID=Europe/Helsinki:20260120T150000
DTSTAMP:20260825T173832
CREATED:20260109T100232Z
LAST-MODIFIED:20260115T092736Z
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SUMMARY:Nicolas BARADEL (Inria) "Constrained deep learning for pricing and hedging European options: towards reinforcement learning extensions."
DESCRIPTION:Finance-Insurance\nTime: 14.00 am\nDate:20h of January 2025\nRoom 3049 \nNicolas BARADEL (Inria) “Constrained deep learning for pricing and hedging European options: towards reinforcement learning extensions.” \nAbstract : In incomplete financial markets\, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. We introduce a constrained deep learning framework to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the option price function\, with its gradient serving as the hedging strategy\, optimized via a loss function that enforces the self-financing portfolio condition. A major difficulty stems from the non-smooth nature of option payoffs (e.g.\, vanilla calls are non-differentiable at-the-money\, digital options are discontinuous)\, which conflicts with the intrinsic smoothness of standard neural networks. To overcome this\, we compare unconstrained architectures with constrained networks that explicitly incorporate the terminal payoff condition\, drawing inspiration from PDE boundary embedding techniques. We further explore an extension of this framework by integrating Howard’s policy iteration algorithm within a reinforcement learning perspective. This direction aims to leverage the efficiency of policy iteration while preserving the terminal payoff constraint through the constrained network architecture. \n  \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/arnaud-germain-univ-catholique-de-louvain-cluster-aggregating-application-to-early-warning-system-for-non-performing-clients-2/
CATEGORIES:Finance-Insurance,Seminars
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