Roberto Casarin

Ca' Foscari University of VeniceVenice Centre in Economic and Risk AnalyticsSan Giobbe 873/b, 30121 Venezia, Italy Office:  Room A125 Phone: +39 041.234.91.49            +39 3297383331 E-mail: r.casarin@unive.itSkype: flubber618Linkedin

Current

I am Professor of Econometrics at Ca' Foscari University of Venice, since 2019 and Director of the Venice Centre in Economic and Risk Analytics VERA. I am a board member of the: European Society of Bayesian Econometrics ESOBE, the PhD program in Economics SSE, the International Master in Economics and Finance IMEF, the Master in Data Analytics for Business and Societies DABS and the Executive Master in Quantum Machine Learning QML.


Background

I was previously an associate and assistant professor at Ca' Foscari University of Venice, University of Brescia, University of Trieste, a research assistant at University of Padova, and a research fellow at GRETA Associates. 

I received a Ph.D. in Mathematics (Mathematical Statistics) in 2008, from University Paris Dauphine and CEREMADE-CNRS, a Ph.D. in Economics (Econometrics) in 2003 from the University Ca' Foscari of Venice, and a M.Sc. in Applied Mathematics from University Paris Dauphine and ENSAE. I have been a visiting professor at the University of Toronto, the University of Paris Sud, the University of Bristol and the University of Paris Dauphine.

Research

It is mainly in Bayesian analysis: Bayesian nonparametrics and semiparametric statistical inference, Monte Carlo simulation methods, time series models, text mining, graphical models, and network analysis. Below is a selection of my research projects.  Former and current Ph.D. students at this link.


I am currently an associate editor of Bayesian analysis and Econometrics and Statistics and a research associate of the centres ECLT, GRETA, member of the scientific societies ISBA, IMS, SSC, ES, SIS and SIdE; the scientific projects HEIRS, HiDiNET, SCSCF, SYRTO and Systemic Risk Hub; and the scientific networks EABCN and ENBIS.

Other

Impact of my research: MathSciNet, ArXiv, GoogleScholar, SSRN, ORCID, Scopus, RePEc. Find me in the Math Genealogy Project with an Erdős number of 3. 

Spare time: I take pleasure in a variety of hobbies, including photography and exploring geology with a pleasantly ordinary proficiency. I often embark on geological explorations in stunning locations, accompanied by my family: Diana, Benedetta, and Riccardo.

Tensor models


Bayesian Nonparametrics and semi-parametrics
Non-Gaussian models
  • Carallo, G., Casarin, R., Robert C.P. (2023), Generalized Poisson Difference Autoregressive Processes, International Journal of Forecasting, forthcoming.
  • Bormetti, G., Casarin, R., Corsi, F. and Livieri, G. (2020), A stochastic volatility model with realized measures for option pricing, Journal of Business & Economic Statistics, 38(4), 856-871.
  • Casarin, R. (2014), Comment on a Tractable State-Space Model for Symmetric Positive-Definite Matrices, Bayesian Analysis, 9(4), 793-804.
  • Casarin, R., Dalla Valle, L. and Leisen, F. (2012), Bayesian Model Selection for Beta Autoregressive Processes, Bayesian Analysis,7(1), 1-26.

Random Fields

  • Casarin, R., Leisen, F., Molina, G. and Ter Horst, E. (2015), Beta Markov Random Field Calibration of the Term Structure of Implied Risk Neutral Densities, Bayesian Analysis, 10(4), 791- 819.
Graphical models
  • Bianchi, D., Billio, M., Casarin, R., Guidolin, M. (2019), Modeling Systemic Risk with Markov Switching Graphical SUR Models, Journal of Econometrics, 210(1), 58-74.
  • Ahelegbey D. F., Billio, M. and Casarin, R. (2016), Bayesian Graphical Models for Structural Vector Autoregressive Processes, Journal of Applied Econometrics, 31(2), 357-386.

Markov switching models
  • Agudze, K. M., Billio, M., Casarin, R., Ravazzolo, F. (2022), Markov Switching Panel with Endogenous Synchronization Effects, Journal of Econometrics, 230(2), 281-298.
  • Casarin, R., Foroni, C., Marcellino, M., Ravazzolo, F. (2018), Economic Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model, Annals of Applied Statistics, 12(4), 2559-2568.
  • Casarin, R., Sartore, D. and Tronzano, M. (2018), A Bayesian Markov-switching correlation model for contagion analysis on exchange rate markets, Journal of Business & Economic Statistics, 36(1), 101-114.

Combination and calibration
  • Casarin, R., Grassi, S., Ravazzolo, F. and van Dijk, H.K. (2023), A Flexible Predictive Density Combination for Large Financial Data Sets in Regular and Crisis Periods, Journal of Econometrics, 237(2), 1-12.
  • Billio, M. and Casarin, R., Ravazzolo, F. and Van Dijk, H.K. (2013), Time-varying Combinations of Predictive Densities using Nonlinear Filtering, Journal of Econometrics, 177(2), 213-232.

Monte Carlo methods
  • Casarin, R., Frattarolo, L., Radu, C., Robert, C. P. (2023), Living on the Edge: An Unified Approach to Antithetic Sampling, Statistical Science, 39(1), 115-136.
  • Casarin, R., Maillet, B., Osuntuyi, A. (2023), Monte Carlo within Simulated Annealing for Integral Constrained Optimizations, Annals of Operations Research, forthcoming.
  • Martino, L., Casarin, R., Leisen, F., Luengo, D. (2018), Adaptive Independent Sticky MCMC algorithms, EURASIP Journal on Advances in Signal Processing, 5, 1-28.
  • Casarin, R., Craiu, R. and Leisen, F. (2016), Embarrassingly Parallel Sequential Markov-chain Monte Carlo for Large Sets of Time Series, Statistics and Its Interface, 9(4), 497-508.
  • Casarin, R., Leisen, F. and Craiu, R. (2013), Interacting Multiple Try Algorithms with Different Proposal Distributions, Statistics and Computing, 23(2), 185-200.
  • Casarin, R. and Marin, J.-M., (2009), Online data processing: Comparison of Bayesian regularized particle filters, Electronic Journal of Statistics, 3, 239-258.
On Evidence
Bassetti, F., Casarin, R., Del Negro, M., (2023) Inference on Probabilistic Surveys. 
In Macroeconomics with an Application to the Evolution of Uncertainty in the Survey of Professional Forecasters during the COVID Pandemic. In van der Klaauw, W., Topa, G. and Bachmann, R. (Eds.), Handbook of Economic Expectations, Elsevier.
Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2022), Matrix-variate Smooth Transition Models for Temporal Networks. 
In Bekker, A., Arashi, M., Chen, D. and Ferreira, J. (Eds.) Innovations in Multivariate Statistical Modelling: Navigating Theoretical and Multidisciplinary Domains, Springer Emerging Topics in Statistics and Biostatistics, Springer Verlag.
Casarin, R. and Veggente, V. (2021), Random Projection Methods in Economics and Finance.
In Petr, H., Uddin, M.M., and Abedin, M. Z. (Eds.), The Essentials of Machine Learning in Finance and Accounting, Chapter 6, Routledge, Taylor & Francis.
Bassetti, F., Casarin, R. and Ravazzolo, F. (2020), Density Forecasting. 
In Fuleky, P. (Ed.), Macroeconomic Forecasting in the Era of Big Data, Springer Verlag, 483-507.