Monica Billio is Full Professor of Econometrics at the University Ca’ Foscari of Venice. She holds a PhD in Applied Mathematics at the University Paris Dauphine. She held visiting research positions at University Paris IX Dauphine, University Paris 1 Pantheon Sorbonne, University of Orleans and Bank of France.
Prof. Billio has published more than 100 technical papers in refereed journals, handbooks, and conference proceedings in the areas of econometrics and financial econometrics, with applications to risk measurement, volatility modelling, financial crisis and systemic risk. She is participating to many research projects financed by the European Commission, Eurostat and the Italian Ministry of Research (MIUR). She has been scientific coordinator of the SYRTO project, EU-FP7 project devoted to systemic risk measurement and she is now local coordinator of two H2020-EE-CSA project on Energy Efficiency (EeMAP and EeDaPP). The results of these and other research projects have appeared in peer-refereed journals including Journal of Econometrics, Journal of Financial Economics, Journal of Applied Econometrics, Journal of Financial Econometrics, Journal of Banking and Finance and European Journal of Operational Research. Prof. Billio is actively involved in the organization of several scientific meetings and, in 2002 she co-established a new series of international workshops devoted to credit and financial risks (CREDIT), which has now reached the seventeenth edition (http://www.greta.it/credit/credit.htm). She is regularly on the program committees of the major international conferences and workshops of her fields and serves on the editorial board for the journal Computational Statistics and Data Analysis and Econometrics and Statistics. She is currently member of the Board of Directors of the European Financial Management Association (EFMA) and member of the Scientific Committee of the Italian Association Financial Industry Risk Managers (AIFIRM).
High dimensional data analysis and modelling; Markov chain probability models; predictive neural networks; machine learning; evolutionary design of experiments, multi-objective optimization.
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Last update: 31/10/2019