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X-WR-CALDESC:Events for CREST
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DTSTART:20250330T010000
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DTSTART;VALUE=DATE:20250623
DTEND;VALUE=DATE:20250704
DTSTAMP:20260710T124744
CREATED:20250620T070901Z
LAST-MODIFIED:20250620T070901Z
UID:18149-1750636800-1751587199@crest.science
SUMMARY:2025 Summer Institute in Computational Social Science
DESCRIPTION:From June 23rd to July 3rd\, 2025 the Institut Polytechnique de Paris will host the Summer Institute in Computational Social Science. It will take place at ENSAE Paris\, 5 Avenue Henri le Chatelier\, Palaiseau\, France (accepted applicants will receive an email with detailed pratical information about accomodation and how to reach the venue). This has been made possible by the generous support of SICSS\, the Templeton Fondation\, CREST\, and Hi!Paris. The purpose of the Summer Institute is to bring together scholars interested in computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived). \nThe Summer Institute is open to social scientists\, computer scientists\, and a few seats could be reserved for people working professionally at this intersection (such as data journalists) if applicable. Please note that although the first 5 days of SICSS-Paris 2025 will be held onsite\, the 4 remaining days will be held remotely. This is to facilitate group work\, and to foster inclusivity. The institute will involve lectures in the morning\, lab sessions in the afternoon\, and about 6 evening guest lectures. During the second week\, the participants will take part in group work aimed at advancing a research project and attend remote guest lectures as well. \nThis year’s institute will focus on Large Language Models and Generative Artificial Intelligence. Sessions will take students all the way from an introduction to text analysis through to practical uses of and critical perspectives on deep learning for text analysis in the social sciences. Participants will have ample opportunities to discuss their ideas and research with the organizers\, with other participants\, as well as with guest speakers. Because we are committed to open and reproducible research\, all materials created for the Summer Institute will be released open-source (find materials from the 2023 edition here). \nParticipation is restricted to advanced Ph.D. students\, postdoctoral researchers\, and junior faculty (within 7 years of their Ph.D). We welcome applicants from all backgrounds and fields of study\, especially junior faculty from neighboring institutions near Palaiseau\, France. About 25-30 participants will be invited. Participants are expected to fully attend and participate in the entire 9-day program\, which includes 5 days onsite and 4 remote\, but we are open to alternative arrangements for faculty members. \nMore information: Summer Institute in Computational Social Science \n
URL:https://crest.science/event/2025-summer-institute-in-computational-social-science/
CATEGORIES:Conferences and Workshops,Sociology
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250703
DTEND;VALUE=DATE:20250704
DTSTAMP:20260710T124744
CREATED:20250611T163829Z
LAST-MODIFIED:20250627T060928Z
UID:18124-1751500800-1751587199@crest.science
SUMMARY:CREST – CentraleSupélec workshop « Mathématiques du risque et de la finance »
DESCRIPTION:Date: July 3rd\, 2025 \nPlace: Bâtiment d’Enseignement Mutualisé\, Room 3 1003 \nProgramme: \n12h30 – 13h30: Déjeuner cocktail (espace de convivialité du 4ème étage coté pôle finance) \n13h30 – 14h00: Ioane Muni-Toke (CentraleSupéléc) « Neural Hawkes: Non-Parametric Estimation in High Dimension and Causality Analysis in Cryptocurrency Markets » \n14h00 – 14h30: Caroline Hillairet (CREST) « Multivariate Self-Exciting Process with Dependencies and application for risk quantification » \n14h30 – 15h00: Jean-David Fermanian (CREST) « Model-based vs. agnostic methods for the prediction of time-varying covariance matrices » \n15h00-15h30: Pause \n15h30 – 16h00: Christian Bongiorno (CentraleSupéléc) « Optimal Covariance Matrix Cleaning with Machine Learning » \n16h00 – 16h30: Gaoyue Guo (CentraleSupéléc) «  Does mutual holding reduce systemic risk? » \n16h30 – 17h00: Jean-François Chassagneux (CREST) « An optimal transport approach to multiple quantile hedging problems » \n  \n
URL:https://crest.science/event/crest-centralesupelec-workshop-mathematiques-du-risque-et-de-la-finance/
CATEGORIES:Conferences and Workshops,Finance-Insurance
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DTSTART;TZID=Europe/Helsinki:20250703T110000
DTEND;TZID=Europe/Helsinki:20250710T120000
DTSTAMP:20260710T124744
CREATED:20250321T110935Z
LAST-MODIFIED:20250702T123749Z
UID:17993-1751540400-1752148800@crest.science
SUMMARY:Paolo ZAFFARONI (Imperial College Business School)  "STATISTICAL ARBITRAGE WITHOUT ARBITRAGE"
DESCRIPTION:Finance-Insurance\nTime: 11.00 am\nDate:03th of July  2025\nRoom 3001 \nPaolo ZAFFARONI (Imperial College Business School) “STATISTICAL ARBITRAGE WITHOUT ARBITRAGE” \nAbstract : Statistical arbitrage strategies are quantitative trading strategies based on alpha\, i.e.\, on the signal extracted from the residuals when fitting an asset pricing model to re- turns data. We develop a normative theory that combines the insights of mean-variance portfolio choice with statistical arbitrage in the no-arbitrage setting of the APT\, that is we study statistical arbitrage without arbitrage . We establish a novel two-fund separation result that combines the inefficient statistical arbitrage portfolio and beta portfolio (i.e.\, the portfolio stemming from factor asset pricing models)\, showing how their combination can span the efficient frontier. In general\, as the number of assets increases\, the “statistical arbitrage portfolio” dominates the “beta portfolio” both in terms of the magnitude of its weights and in terms of its SR. Exploiting some insights of mean-variance portfolio choice\, we show how to construct a special statistical arbitrage portfolio that does not require estimating alpha\, in contrast to the common view. When statistical arbitrage is combined with factor asset pricing modelling\, alpha and the factors’ risk premia might not be jointly identified\, jeopardizing the possibility to construct a statistical arbitrage portfolio. We derive a penalized estimator under a no-arbitrage constraint that allows for the econometric identification of the statistical arbitrage portfolio\, leading to a shrinkage-type estimator. We demonstrate our theoretical insights by means of Monte Carlo experiments and empirical applications. \n[joint work with Massimo Dello Preite and Valentina Raponi]\n \nOrganizers:  Zakoian Jean-Michel \n  \n
URL:https://crest.science/event/paolo-zaffaroni-imperial-college-business-school-t-b-a/
CATEGORIES:Finance-Insurance,Seminars
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