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X-WR-CALDESC:Events for CREST
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TZID:Europe/Helsinki
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260205T100000
DTEND;TZID=Europe/Helsinki:20260205T230000
DTSTAMP:20260711T150249
CREATED:20260202T075730Z
LAST-MODIFIED:20260202T075730Z
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SUMMARY:Katerina PETROVA (Federal Reserve Bank of New-York & Univ. Pompeu Fabra) "Uniform inference with general autoregressive processes"
DESCRIPTION:Finance-Insurance\nTime: 10.00 am\nDate:05th of February 2026\nRoom 3001 \nKaterina PETROVA (Federal Reserve Bank of New-York & Univ. Pompeu Fabra) “Uniform inference with general autoregressive processes” \nAbstract : A unified theory of estimation and inference is developed for an autoregressive process with root in (-∞\, ∞) that includes the stationary\, local-to-unity\, explosive and all intermediate regions. The discontinuity of the limit distribution of the t-statistic outside the stationary region and its dependence on the distribution of the innovations in the explosive regions (-∞\, -1) ∪ (1\, ∞) are addressed simultaneously. A novel estimation procedure\, based on a data-driven combination of a near-stationary and a mildly explosive artificially constructed instrument\, delivers mixed-Gaussian limit theory and gives rise to an asymptotically standard normal t-statistic across all autoregressive regions. The resulting hypothesis tests and confidence intervals are shown to have correct asymptotic size (uniformly over the space of autoregressive parameters and the space of innovation distribution functions) in autoregressive\, predictive regression and local projection models\, thereby establishing a general and unified framework for inference with autoregressive processes. Extensive Monte Carlo simulation shows that the proposed methodology exhibits very good finite sample properties over the entire autoregressive parameter space (-∞\, ∞) and compares favorably to existing methods within their parametric (-1\, 1] validity range. We demonstrate how our procedure can be used to construct valid confidence intervals in standard epidemiological models as well as to test in real-time for speculative bubbles in the price of the Magnificent Seven tech stocks. \nOrganizers:  Jean-Michel ZAKOIAN & Christian FRANCQ \n  \n
URL:https://crest.science/event/katerina-petrova-federal-reserve-bank-of-new-york-univ-pompeu-fabra-uniform-inference-with-general-autoregressive-processes/
CATEGORIES:Finance-Insurance,Seminars
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DTSTART;TZID=Europe/Helsinki:20260205T110000
DTEND;TZID=Europe/Helsinki:20260205T120000
DTSTAMP:20260711T150249
CREATED:20260202T080049Z
LAST-MODIFIED:20260202T080049Z
UID:18775-1770289200-1770292800@crest.science
SUMMARY:Gero JUNIKE (LMU\, Munich) "Accuracy estimation of neural networks by extreme value theory\, validation of generative AI and applications to finance"
DESCRIPTION:Finance-Insurance\nTime: 11.00 am\nDate:05th of February 2026\nRoom 3001 \nGero JUNIKE (LMU\, Munich) “Accuracy estimation of neural networks by extreme value theory\, validation of generative AI and applications to finance” \nAbstract : In the first part of the talk\, we consider a random variable Y_k that depends on a parameter k. For example\, in a financial application\, Y_k could represent the payoff of a financial contract under a certain model. The parameter k describe the contract and the model. For each k\, we are interested in the expectation of Y_k\, which can be interpreted as a price. This expectation can be obtained using Monte Carlo methods or Fourier techniques. To increase computational speed\, researchers proposed using a neural network to learn the map f(k) := E[Y_k]. This is possible because neural networks can approximate any continuous function on a compact set. We propose using extreme value theory to quantify large values of the approximation error by the neural network. Large errors are typically relevant in financial applications. In the second part of the talk\, we will not assume that the random variable of interest is given by a model anymore. We are only given M realizations. Using generative AI\, we learn the distribution of the random variable to generate new samples. A possible application is determining the value-at-risk of a financial portfolio. We discuss the unwanted memorization effect (overfitting) of generative AI. We introduce a novel memorization ratio to detect this effect and apply it to an autoencoder-based scenario generator. \nOrganizers:  Jean-Michel ZAKOIAN & Christian FRANCQ \n  \n
URL:https://crest.science/event/gero-junike-lmu-munich-accuracy-estimation-of-neural-networks-by-extreme-value-theory-validation-of-generative-ai-and-applications-to-finance/
CATEGORIES:Finance-Insurance,Seminars
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DTSTART;TZID=Europe/Helsinki:20260205T120000
DTEND;TZID=Europe/Helsinki:20260205T133000
DTSTAMP:20260711T150249
CREATED:20260121T130417Z
LAST-MODIFIED:20260202T080642Z
UID:18744-1770292800-1770298200@crest.science
SUMMARY:Olivier GODECHOT (Sciences Po) - "Return on Team Moves"
DESCRIPTION:Sociology Seminar \nTime: 12:00 pm – 13:30 pm\nDate: 5th of february\nRoom : 3049 \n  \nOlivier GODECHOT  (Sciences Po) – “Return on Team Moves” \n  \nAbstract :  \nLabor market mobility is often viewed as an individual phenomenon\, driven by a worker’s essential productivity—an outcome of innate talent and accumulated human capital prior to entering the labor market. In the classical framework\, the exit option reveals a worker’s market value and\, by extension\, their individual productivity. But what if mobility is not only individual\, but also collective? This occurs when a firm hires an already-formed team from another firm. In such cases\, the output of the group exceeds the sum of its members’ individual human capital. Team moves\, therefore\, challenge the classical individualistic and essentialist view of labor markets. Previous research (Lazega 2001; Groysberg 2010; Godechot 2017) has shown that team moves play a key role in high-end professions such as law and finance. Drawing on qualitative fieldwork and interviews in these sectors\, I document how team moves facilitate the transfer of business activity—especially clients—from one firm to another. To test whether workers are compensated for this transfer\, I analyze the French administrative wage dataset (BTS) for managers and professionals in the Paris region. I identify team moves as instances where three to fifteen individuals simultaneously move between firms within the same industry. Using LP-DID staggered difference-in-differences methods\, I find that team moves lead to substantial wage gains. Two years post-move\, team movers earn 10–12% more than stayers and 3–5% more than solo movers. In finance\, the premium is even higher: 20% over stayers and 10% over solo movers. More broadly\, team moves are more lucrative than solo moves in sectors where clients are the primary assets\, as opposed to those where knowledge and expertise dominate. This suggests that team mobility is an effective mechanism for transferring clients between firms. Ultimately\, team moves contribute to the reconfiguration of capitalism by enabling groups of highly skilled workers to leverage their collective power\, challenge firm boundaries\, and reallocate key assets—especially clients—in pursuit of their own interests. \nZoom link : https://zoom.us/j/96164989399?pwd=AYqhb23SGkaVKS6WaMrkL6jJCOK4bw.1 \n  \n  \nOrganizers:\nPaola TUBARO (Pôle sociologie CREST) \nNicolas JULIA (Pôle sociologie CREST) \nPatrick PRÄG (Pôle sociologie CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-www-sciencespo-fr-cris-fr-annuaire-godechot-olivier/
CATEGORIES:Seminars,Sociology
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