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Fwd: [owps] Reminder next OWPS
by Faggionato, Alessandra 27 May '24

27 May '24

Buongiorno ricevo e con piacere inoltro. Saluti Alessandra ---------- Forwarded message --------- From: One World Probability <ow.probability(a)gmail.com> Date: Mon, 27 May 2024 at 08:40 Subject: [owps] Reminder next OWPS To: <owps(a)lists.bath.ac.uk> This is a gentle reminder that the next OWPS will be *today* from *15:00* to *17:00* *CEST *time. There will be two talks on the topic of exploration driven analysis of random graphs. The talks will touch upon general techniques and specific applications of these techniques. Title, abstract and the zoom link are below the signature and can be found on the website https://www.owprobability.org/one-world-probability-seminar <https://protect-eu.mimecast.com/s/-zGkCWqjZFlpkVlsnEyR_?domain=eur01.safeli…> . ----------------------- Talk 1 :* Mariana Olvera-Cravioto, University of North Carolina at Chapel Hill* Local limits and preferential attachment graphs This talk is meant to provide an overview of local weak convergence techniques for a large class of directed random graphs, including static models such as the Erdos-Renyi, Chung-Lu, stochastic block model, and configuration models, as well as dynamic models such as the collapsed branching process and the directed preferential attachment graph. We explain how local limits can be used to study important structural graph properties, including centrality measures such as the PageRank distribution. We further use the insights obtained from our analysis of centrality measures to explain how static models and evolving models differ in how large degree vertices are distributed within the graph and how they shape their neighborhoods. In particular, we show that the empirically accepted “power-law hypothesis” on scale-free graphs, which states that the PageRank distribution follows a power-law with the same tail index as the in-degree distribution, holds in most static models but not in the dynamic models we study. This is joint work with: Sayan Banerjee and Prabhanka Deka. Talk 2 : Sayan Banerjee, *University of North Carolina at Chapel Hill* Exploration-driven networks We propose and investigate a class of random networks where incoming vertices locally explore the graph before attaching to an existing vertex. Specific instances of these networks correspond to uniform attachment, linear preferential attachment and attachment with probability proportional to vertex PageRanks. We obtain local weak limits for such networks and use them to derive asymptotics for the limiting empirical degree and PageRank distribution. We also quantify asymptotics for the degree and PageRank of fixed vertices, including the root, and the height of the network. Two distinct regimes are seen to emerge, based on the expected exploration distance of incoming vertices, which we call the ‘fringe’ and ‘non-fringe’ regimes. These regimes are shown to exhibit different qualitative and quantitative properties. In particular, networks in the non-fringe regime undergo ‘condensation’ where the root degree grows at the same rate as the network size. Networks in the fringe regime do not exhibit condensation. Interesting phase transition phenomena are exhibited for the height of the tree and the limiting PageRank distribution. The latter connects to the well-known power-law hypothesis and the proposed class of models `interpolate’ between the PageRank behavior of static and dynamic graphs discussed in Mariana’s talk. Based on joint works with Mariana Olvera-Cravioto, Shankar Bhamidi and Xiangying (Zoe) Huang. Zoom-link: https://zoom.us/j/3766827761 Meeting ID: 376 682 7761 Passcode: sPNKq1 -- ************************************************* Prof. Alessandra Faggionato https://www1.mat.uniroma1.it/people/faggionato/ Department of Mathematics University "La Sapienza" Piazzale Aldo Moro, 5 00185 - Rome Office 123, first floor *************************************************

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talk Prof. Domenico Marinucci on May 27 - DIMA - UNIGE
by Ernesto De Vito 24 May '24

24 May '24

Dear all, this is a kind reminder of the following seminar Speaker : Domenico Marinucci (https://sites.google.com/view/domenicomarinucci/home) Affiliation: Dipartimento di Matematica - Università Roma Tor Vergata Title: Spectral complexity of deep neural networks Date: Monday, May 27, 2024 at 11.30 Place: Aula 704 (7th floor) , Dipartimento di Matematica at University of Genova, Via Dodencaneso 35, Teams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NTEyMmU4MWEtZjIwOC00… Abstract: It is well-known that randomly initialized, push-forward, fully-connected neural networks weakly converge to isotropic Gaussian processes, in the limit where the width of all layers goes to infinity. In this paper, we propose to use the angular power spectrum of the limiting fields to characterize the complexity of the network architecture. In particular, we define sequences of random variables associated with the angular power spectrum, and provide a full characterization of the network complexity in terms of the asymptotic distribution of these sequences as the depth diverges. On this basis, we classify neural networks as low-disorder, sparse, or high-disorder; we show how this classification highlights a number of distinct features for standard activation functions, and in particular, sparsity properties of ReLU networks. Our theoretical results are also validated by numerical simulations. Joint work with Simmaco Di Lillo, Michele Salvi e Stefano Vigogna Best wishes, Ernesto --------------------- Ernesto De Vito DIMA - Dipartimento di Matematica MaLGa - Machine Learning Genoa center Via Dodecaneso 35 16146 Genova Italy e-mail: ernesto.devito(a)unige.it tel: +390103536783

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talk Dott. Marco Tarsia on May 29, 2024
by Mastrogiacomo Elisa 24 May '24

24 May '24

Dear all, you are invited to participate to the following seminar on Wednesday 29th of May 2024 at 12:00: Title: STOCHASTIC ORDERINGS FOR SET-VALUED RISK MEASURES Abstract: We introduce and analyze from an axiomatic point of view an extension to the set-valued scenario of the maximal correlation risk measure as defined by Burgert and Rüschendorf (2006). Furthermore, we present the notion of stochastic ordering for random vectors, utilizing the upper expectation operator - introduced in Hamel and Heyde (2021) - in conjunction with extensive classes of multidimensional functions. We then explore the consistency of such stochastic orderings for appropriate set-valued risk measures. These measures resemble the maximal correlation risk measure, offering flexibility regarding adherence to all the axioms of a portfolio aggregator and a proper set-valued risk measure. A remarkable example within this category is represented by law-invariant set-valued risk measures, such as the set-valued distortion risk measure as defined, for example, in Chen and Hu (2019). The in-person presentation will be held in Room 3 Padiglione Morselli, Via Ottorino Rossi, 9, 21100 Varese VA<https://www.google.com/maps/place//data=!4m2!3m1!1s0x478681f7697827db:0x771…>, Insubria University. Scan the QR code in the attached file to join the seminar online in MS Teams. Best regards, Elisa Mastrogiacomo Elisa Mastrogiacomo ----------------------------------------- Professore Ordinario di Metodi matematici dell'economia e delle scienze attuariali e finanziarie DIPARTIMENTO D'ECCELLENZA 2023/2027 Università degli Studi dell'Insubria Dipartimento di Economia Via Monte Generoso, 71 – 21100 Varese tel. +39 0332/395528 web: www.uninsubria.it<http://www.uninsubria.it/> mail: elisa.mastrogiacomo(a)uninsubria.it<mailto:mario.rossi@uninsubria.it> Chiaramente Insubria! [LOGO-ATENEO-FONDO TRASPARENTE]<http://www.uninsubria.it/> [Facebook] <https://www.facebook.com/uninsubria> [Twitter] <https://twitter.com/Uni_Insubria> [Instagram] <https://instagram.com/uninsubria> [YouTube] <https://www.youtube.com/user/LabVAMultimedia>

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JOB: Research Associate in Bayesian Statistics, Newcastle University
by Fabrizio Leisen 24 May '24

24 May '24

Con preghiera di diffusione. IMPORTANTE: Il candidato deve gia avere un dottorato di ricerca. Non sono considerati candidati che non hanno ancora ottenuto (o stanno per ottenere) un dottorato di ricerca. È richiesta buona conoscenza di Statistica Bayesiana e metodi computazionali. È una posizione di due anni basata a Newcastle. Il progetto sarà supervisionato da me, Prof. Cristiano Villa e Prof. Kevin Wilson. Cordiali Saluti, Fabrizio -- Fabrizio Leisen Professor of Statistics King’s College London Department of Mathematics Strand | WC2R 2LS | London JOB: Research Associate in Bayesian Statistics School of Mathematics, Statistics and Physics, Newcastle University Full advert: https://jobs.ncl.ac.uk/job/Newcastle-Research-Associate-in-Bayesian-Statist… Applications are invited from outstanding individuals for a Research Associate in Bayesian Statistics, to start from August/September 2024 for a period of two years. The project is at the forefront of Bayesian inference and aims to design a novel Bayesian methodology to implement Bayesian Additive Regression Trees (BART) models to deal with non-linear phenomena. In particular, the aim is to bridge the Loss-based methodology to the framework of BART, building on the previous works of Dr Cristiano Villa and Prof. Fabrizio Leisen on model selection, linear regression models and Gaussian graphical models. The successful candidate will work on developing prior distributions to estimate the structure of the trees and the number of trees in a BART model, and building scalable and efficient computational tools to ease the implementation of the methodology. The candidate must have a solid background in computational methods for Bayesian inference to develop efficient algorithms for the new proposed modelling framework. Within the project, the candidate will also perform theoretical modelling and data analysis, as well as disseminate the outputs through publications and presentations at scientific meetings. The successful candidate will join the growing Statistics group at Newcastle University and interact with the Statistics group at King's College London. The position is offered on a fixed term basis for two years from the start date, or tenable until the project end date, whichever is soonest. Generous funds are available for travel, training and other support. To apply, please complete the online application and attach a CV and covering letter by the deadline of 23rd June 2024. In your covering letter, please outline how you are either working towards, meet or exceed all the essential requirements for the role holder as outlined in the full advertisement, and highlight any expertise relevant to the project. For all information enquiries about the position, please contact Dr Kevin Wilson (kevin.wilson(a)newcastle.ac.uk<mailto:kevin.wilson@newcastle.ac.uk>), Prof Cristiano Villa (cristiano.villa(a)dukekunshan.edu.cn<mailto:cristiano.villa@dukekunshan.edu.cn>) or Prof. Fabrizio Leisen (fabrizio.leisen(a)kcl.ac.uk<mailto:fabrizio.leisen@kcl.ac.uk>). Kind Regards Kevin Dr Kevin Wilson Reader in Applied Statistics School of Mathematics, Statistics & Physics Newcastle University kevin.wilson(a)newcastle.ac.uk You may leave the list at any time by sending the command SIGNOFF allstat to listserv(a)jiscmail.ac.uk, leaving the subject line blank. -- Fabrizio Leisen Professor of Statistics King’s College London Department of Mathematics Strand | WC2R 2LS | London

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UPDATED: Time Series Models for Financial and Environmental Risk - May 30 - Ca' Foscari University
by Roberto CASARIN 21 May '24

21 May '24

Dear Colleagues, On Thursday the 30th of May you are all warmly invited to our one-day workshop organised under the GRINS Project on *Time Series Models for Financial and Environmental Risk* Participation is free. However, for organizational purposes, you are kindly asked to register by May the 27th here https://bit.ly/form-workshop-timeseriesmodels . Ca’ Foscari University, San Giobbe Economics Campus, Meeting Room 1, Venezia *Program:* 10.00am Registration 10.45am *Welcome Remarks: Prof. Monica Billio, Ca’ Foscari University of Venice, Project Leder of the Grins Spoke 4, Sustainable Finance.* *1st Session* 11.00am *Prof.* *Giacomo Bormetti, University of Pavia*, *Measuring price impact and information content of trades in a time-varying setting.* 11.45am *Dr. Giuseppe Buccheri, University of Verona*, *Semiparametric Estimation of Volatility in the Presence of Intraday Drift Dynamics.* 12.30pm Lunch *2nd Session* 2.00pm* Dr. Dario Palumbo, Ca' Foscari University of Venice*, *An Observation-Driven Generalized Poisson Model*. 2.45pm *Dr. Enzo D'Innocenzo, University of Bologna, **Limit results for score-driven filters**.* 3.30pm Coffee break 4.00pm *Keynote speech**: Prof. Andrew Harvey, University of Cambridge. **Score-driven models: overview and recent developments.* *Abstracts*: *Prof.* *Giacomo Bormetti,*University of Pavia, Italy https://unipv.unifind.cineca.it/get/person/021642 *Measuring price impact and information content of trades in a time-varying setting* We propose a non-linear observation-driven version of the Hasbrouck (1991) model for dynamically estimating trades' market impact and information content. We find that market impact displays an intraday pattern superimposed with large fluctuations. Some of them are exogenous, and, as an example, we investigate market impact dynamics around FOMC announcements. Contrary to Hasbrouck (1991), we find that the information content of trades depends on the local liquidity level and the recent history of prices and trades. Finally, we use the model to estimate the time-varying permanent impact parameter, which allows performing a dynamic transaction cost analysis. Joint work with Francesco Campigli and Fabrizio Lillo. *Dr. Giuseppe Buccheri,*University of Verona, Italy https://sites.google.com/view/giuseppe-buccheri/home *Semiparametric Estimation of Volatility in the Presence of Intraday Drift Dynamics* In the conventional semimartingale representation of the log-price of a financial security, the volatility of daily log-returns is consistently estimated by their ex-post quadratic variation. A crucial assumption underlying this result is that the drift dynamics are negligible or pre-determined given past information. Recent empirical evidences of intraday unanticipated variations of mean returns call into doubt the validity of this assumption. We propose a semiparametric estimator of the daily return volatility incorporating the effect of stochastic intraday dynamics of the drift process. This framework allows us to test for the presence of drift dynamics in the data and to assess their impact on the daily return volatility. Our empirical analysis shows three main results: (i) there exists compelling evidence of intraday drift dynamics across various asset classes; (ii) when the drift moves significantly, our measure of volatility provides a better description of the return distribution; (iii) the component of the return variance associated with drift dynamics is non-negligible and possesses predictive power. Joint work with Giorgio Vocalelli. *Dr. Dario Palumbo,* Ca' Foscari University of Venice, Italy https://www.unive.it/data/people/22437798 *An Observation-Driven Generalized Poisson Model for Environmental Variables* Based on the Generalised Poisson (GP) conditional distribution, a new general class of observation-driven models for count data is presented and their theoretical properties are derived. The GP is a flexible distribution which allows for both under- and over-dispersion. As a special member of this class, a score-driven model version is introduced. We show that this specification is robust to the presence of outliers and can be extended to allow for time varying over-dispersion. For the estimation of the model we provide a Bayesian inference framework and an efficient posterior approximation procedure based on Markov Chain Monte Carlo. The applications on environmental variables show that the proposed model is well suited for capturing the over-dispersion feature of the data. *Dr. Enzo D'Innocenzo,* University of Bologna, Italy https://www.enzodinnocenzo.com/ *Limit results for score-driven filters* A study of the filtering approximation-theoretic properties of score-driven time series models. The new results are derived under a set of Lipschitz-type and tail conditions, from which I obtain maximal and deviation inequalities for the filtering approximation error. For the latter, I use techniques from empirical process theory. The novel proposed approach allows, for the first time, to study the asymptotic behavior of the empirical distribution function and the empirical processes of the approximated residuals. Since score-driven models encompasses the well-known generalized autoregressive conditional heteroskedasticity, it is proved that the results obtained in this paper extends those of Francq and Zakoian (2022) for this class of nonlinear models. In conclusion, I also give an application of these results to a popular score-driven time series models, namely, the Beta-t-GARCH(1,1) model. *Prof. Andrew Harvey,*University of Cambridge, UK *https://www.econ.cam.ac.uk/people/emeritus/ach34 <https://www.econ.cam.ac.uk/people/emeritus/ach34>* *Score-driven models: overview and recent developments* Score-driven <ach34(a)cam.ac.uk)Score-driven> time series models were introduced into the literature more than a decade ago. Since then progress has been made on the theory and a variety of new models have been introduced. I begin by reviewing theoretical results on stationarity and invertibility and show how these apply to moment-driven and score-driven models for dynamic location and/or scale parameters from a wide range of distributions. Issues of robustness are discussed and it is argued that quasi score-driven (QSD) models have an important role to play. The advantages of an exponential link function are stressed. In particular it is argued that (Q)SD-EGARCH models are much better than standard (moment-driven) GARCH models. I finish by showing how the score-driven approach solves two difficult problems: constructing time series models for censored observations and circular data. -- Roberto Casarin, PhD Professor of Econometrics Ca' Foscari University of Venice San Giobbe 873/b - 30121 Venezia, Italy http://sites.google.com/view/robertocasarin/ https://www.unive.it/vera <https://www.unive.it/isba2024> https://www.unive.it/isba2024

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Time Series Models for Financial and Environmental Risk - May 30 - Ca' Foscari University
by Roberto CASARIN 21 May '24

21 May '24

Dear Colleagues, On Thursday the 30th of May you are all warmly invited to our one-day workshop organised under the GRINS Project on *Time Series Models for Financial and Environmental Risk* *https://www.unive.it/data/33113/3/88487 <https://www.unive.it/data/agenda/3/88487>* Participation is free. However, for organizational purposes, you are kindly asked to register by May the 27th here https://bit.ly/form-workshop-timeseriesmodels . Ca’ Foscari University, San Giobbe Economics Campus, Meeting Room 1, Venezia *Program:* 10.00am Registration 10.45am *Welcome Remarks: Prof. Monica Billio, Ca’ Foscari University of Venice, Project Leder of the Grins Spoke 4, Sustainable Finance.* *1st Session* 11.00am *Prof.* *Giacomo Bormetti, University of Pavia*, *Measuring price impact and information content of trades in a time-varying setting.* 11.45am *Dr. Giuseppe Buccheri, University of Verona*, *Semiparametric Estimation of Volatility in the Presence of Intraday Drift Dynamics.* 12.30pm Lunch *2nd Session* 2.00pm* Dr. Dario Palumbo, Ca' Foscari University of Venice*, *An Observation-Driven Generalized Poisson Model*. 2.45pm *Dr. Enzo D'Innocenzo, University of Bologna, **Limit results for score-driven filters**.* 3.30pm Coffee break 4.00pm *Keynote speech**: Prof. Andrew Harvey, University of Cambridge. **Score-driven models: overview and recent developments.* *Abstracts*: *Prof.* *Giacomo Bormetti,*University of Pavia, Italy https://unipv.unifind.cineca.it/get/person/021642 <https://warwick.ac.uk/dfirth> *Measuring price impact and information content of trades in a time-varying setting* We propose a non-linear observation-driven version of the Hasbrouck (1991) model for dynamically estimating trades' market impact and information content. We find that market impact displays an intraday pattern superimposed with large fluctuations. Some of them are exogenous, and, as an example, we investigate market impact dynamics around FOMC announcements. Contrary to Hasbrouck (1991), we find that the information content of trades depends on the local liquidity level and the recent history of prices and trades. Finally, we use the model to estimate the time-varying permanent impact parameter, which allows performing a dynamic transaction cost analysis. Joint work with Francesco Campigli and Fabrizio Lillo. *Dr. Giuseppe Buccheri,*University of Verona, Italy https://sites.google.com/view/giuseppe-buccheri/home <https://www.ikosmidis.com/> *Semiparametric Estimation of Volatility in the Presence of Intraday Drift Dynamics* In the conventional semimartingale representation of the log-price of a financial security, the volatility of daily log-returns is consistently estimated by their ex-post quadratic variation. A crucial assumption underlying this result is that the drift dynamics are negligible or pre-determined given past information. Recent empirical evidences of intraday unanticipated variations of mean returns call into doubt the validity of this assumption. We propose a semiparametric estimator of the daily return volatility incorporating the effect of stochastic intraday dynamics of the drift process. This framework allows us to test for the presence of drift dynamics in the data and to assess their impact on the daily return volatility. Our empirical analysis shows three main results: (i) there exists compelling evidence of intraday drift dynamics across various asset classes; (ii) when the drift moves significantly, our measure of volatility provides a better description of the return distribution; (iii) the component of the return variance associated with drift dynamics is non-negligible and possesses predictive power. Joint work with Giorgio Vocalelli. *Dr. Dario Palumbo,* Ca' Foscari University of Venice, Italy https://www.unive.it/data/people/22437798 *An Observation-Driven Generalized Poisson Model for Environmental Variables* Based on the Generalised Poisson (GP) conditional distribution, a new general class of observation-driven models for count data is presented and their theoretical properties are derived. The GP is a flexible distribution which allows for both under- and over-dispersion. As a special member of this class, a score-driven model version is introduced. We show that this specification is robust to the presence of outliers and can be extended to allow for time varying over-dispersion. For the estimation of the model we provide a Bayesian inference framework and an efficient posterior approximation procedure based on Markov Chain Monte Carlo. The applications on environmental variables show that the proposed model is well suited for capturing the over-dispersion feature of the data. *Dr. Enzo D'Innocenzo,* University of Bologna, Italy https://www.enzodinnocenzo.com/ <https://researchportal.uc3m.es/display/inv16189> *Limit results for score-driven filters* A study of the filtering approximation-theoretic properties of score-driven time series models. The new results are derived under a set of Lipschitz-type and tail conditions, from which I obtain maximal and deviation inequalities for the filtering approximation error. For the latter, I use techniques from empirical process theory. The novel proposed approach allows, for the first time, to study the asymptotic behavior of the empirical distribution function and the empirical processes of the approximated residuals. Since score-driven models encompasses the well-known generalized autoregressive conditional heteroskedasticity, it is proved that the results obtained in this paper extends those of Francq and Zakoian (2022) for this class of nonlinear models. In conclusion, I also give an application of these results to a popular score-driven time series models, namely, the Beta-t-GARCH(1,1) model. *Prof. Andrew Harvey,*University of Cambridge, UK *https://www.econ.cam.ac.uk/people/emeritus/ach34 <https://www.econ.cam.ac.uk/people/emeritus/ach34>* *Score-driven models: overview and recent developments* Score-driven <ach34(a)cam.ac.uk)Score-driven> time series models were introduced into the literature more than a decade ago. Since then progress has been made on the theory and a variety of new models have been introduced. I begin by reviewing theoretical results on stationarity and invertibility and show how these apply to moment-driven and score-driven models for dynamic location and/or scale parameters from a wide range of distributions. Issues of robustness are discussed and it is argued that quasi score-driven (QSD) models have an important role to play. The advantages of an exponential link function are stressed. In particular it is argued that (Q)SD-EGARCH models are much better than standard (moment-driven) GARCH models. I finish by showing how the score-driven approach solves two difficult problems: constructing time series models for censored observations and circular data. -- Roberto Casarin, PhD Professor of Econometrics Ca' Foscari University of Venice San Giobbe 873/b - 30121 Venezia, Italy http://sites.google.com/view/robertocasarin/ https://www.unive.it/vera <https://www.unive.it/isba2024> https://www.unive.it/isba2024

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Summer School on "Computational Methods in Finance and Economics", 9-13 September 2024, San Miniato, Italy.
by Giulio Bottazzi 21 May '24

21 May '24

Dear Colleagues, the Centro de Giorgi, Scuola Normale Superiore, and the Institute of Economics of Scuola Superiore Sant'Anna, are jointly organizing the Ph.D.Summer School on "Computational Methods in Finance and Economics". The school is aimed at graduate students in Economics, Finance and related disciplines. For information and application please check https://crm.sns.it/event/528/ Best Regards, Giulio Bottazzi -- Giulio Bottazzi PGP Key ID:BAB0A33F Professor of Economics Faculty of Social Sciences Advanced Calculus for Economics and Finance Theory and Methods https://link.springer.com/book/10.1007/978-3-031-30316-6 Scuola Superiore Sant'Anna 56127 - Pisa, Italy Phone:(+39)-050-883365 email:bottazzi@santannapisa.it Fax: (+39)-050-883344 http://www.sssup.it/~bottazzi The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review of, re-transmission, dissemination or other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient, is prohibited. If you received this in error, please inform the sender by e-mail and delete the content. Il contenuto e gli allegati di questo messaggio sono strettamente confidenziali e ad uso esclusivo del destinatario. Ne sono vietati la diffusione, l'inoltro e l'uso non autorizzato da parte di utenti diversi dal destinatario. Qualora il presente messaggio Le fosse pervenuto per errore, La preghiamo di distruggerlo e darcene comunicazione a mezzo e-mail.

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seminar PESCE
by Stefano Pagliarani 20 May '24

20 May '24

Dear all, On May 22nd, in the classroom Aula Arzelà, Department of Mathematics of the University of Bologna, the following seminars will take place: * 11:00-12:00: Antonello PESCE "OPTIMAL IRREVERSIBLE INVESTMENTS WITH PRICE IMPACT IN A COMPETING MARKET: A SINGULAR STOCHASTIC GAME" * 12:00-13:00: Fabrizio LILLO "A NOISY DYNAMICAL SYSTEM MODEL OF FINANCIAL SYSTEMIC RISK" as part of the cycle Stochastics and Applications. The abstracts are available at https://www.dm.unibo.it/seminari/mat/serials/36 For any question, please contact the organizers: Lorenzo Cerboni Baiardi and Stefano Pagliarani. ------------------------------------------------------------------------------------------------------------------------ Stefano Pagliarani, Associate Professor Piazza di Porta S. Donato, 5 University of Bologna (Alma Mater) 40126 Bologna (BO), Italy Department of Mathematics Cell. Phone +39 366 5013755 Email: stepagliara1(a)gmail.com<mailto:stepagliara1@gmail.com>, <mailto:stefano.pagliarani9@unibo.it> stefano.pagliarani9(a)unibo.it<mailto:stefano.pagliarani9@unibo.it> Zoom: https://unibo.zoom.us/j/3755841669 -----------------------------------------------------------------------------------------------------------------------

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Seminario 4 giugno e visita a Milano Bandyopadhyay Antar
by Giacomo Aletti 20 May '24

20 May '24

Nell’ambito dei progetti di eccellenza, il Prof. Bandyopadhyay Antar Theoretical Statistics and Mathematics Unit Head of Indian Statistical Institute, Delhi Centre, New Delhi, India sarà a Milano nella settimana 2-9 giugno. È stato organizzato un seminario per il 4 giugno 2024 dalle ore 14.30 alle ore 15.30 in Aula Maggiore, via Celoria 2, Milano, dal titolo A classical random reinforcement model viewed differently! Abstract: In this talk, we will start by explaining a classical random reinforcement model introduced by Pólya back in 1920 which is widely known as the Pólya's urn scheme. We will then indicate several unrelated examples can be thought as following Pólya's model provided we allow ourselves a novel infinite dimensional generalization. After indicating the difficulties which arise due to absence of good mathematical (mostly algebraic) techniques to deal with infinite dimensional matrices (or appropriate linear operators), we will explain a new probabilistic technique to handle the infinite dimensional generalization. We shall define a novel stochastic process which will turn out to be Markov with respect to a new type of "time'' parameter, which instead of deterministic and unidirectional, will be random and will have a tree-type structure. We shall show that this new process and the infinite dimensional generalization of Pólya's balanced urn scheme have the same law. This will enable us to derive fairly general scaling limits for the infinite dimensional scheme and show that classical results can be easily derived without much difficulties and completely avoiding algebraic techniques. Moreover, we will show that apparently unrelated problems arising from a variety of different fields such as combinatorics, statistical physics and statistics, can be solved by using this new generalization. Il seminario è pensato per un pubblico non specialistico ed è aperto a tutti.

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talk Prof. Nacira Agram on May 23, 2024 - REMINDER
by Emanuela Rosazza Gianin 20 May '24

20 May '24

Dear all, you are all invited to participate to the following seminar on *Thursday 23th of May 2024 at 15:00* in Aula Seminari Demografica 2062 at University of Milano Bicocca (via degli Arcimboldi 8, Milano - building U7, 2nd floor): Speaker: *Nacira Agram (KTH Stockholm)* Titolo: *Deep learning for conditional McKean-Vlasov Jump diffusions* Abstract: My talk focuses on using deep learning methods to optimize the control of conditional McKean-Vlasov jump diffusions. We begin by exploring the dynamics of multi-particle jump-diffusion and presenting the propagation of chaos. The optimal control problem in the context of conditional McKean-Vlasov jump-diffusion is introduced along with the verification theorem (HJB equation). A linear quadratic conditional mean-field (LQ CMF) is discussed to illustrate these theoretical concepts. Then, we introduce a deep-learning algorithm that combines neural networks for optimization with path signatures for conditional expectation estimation. The algorithm is applied to practical examples, including LQ CMF and interbank systemic risk, and we share the resulting numerical outcomes. Based on joint work with Jan Rems. Best wishes, Emanuela ****************************************** Emanuela Rosazza Gianin Dipartimento di Statistica e Metodi Quantitativi Università di Milano-Bicocca Tel. 02 64483208 e-mail: emanuela.rosazza1(a)unimib.it ******************************************

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