Please note that the following Summer School has been postponed until further notice due to the latest development of the COVID-19 outbreak.
SoFiE Financial Econometrics Summer School
"The Econometrics of Mixed Frequency (Big) Data"
July 20-July 24, 2020
Volatility Institute, NYU Shanghai,
1555 Century Avenue, Pudong, Shanghai, China, 200122
The SoFiE Financial Econometrics Schools are annual week-long research-based courses for Ph.D. students and new faculty in financial econometrics. For the first two years, the Summer School was held at Oxford University’s Oxford-Man Institute and in 2014 it moved to Harvard University. In 2015 and 2016, it was held in Brussels. In 2017, The SoFiE Financial Econometrics Summer School took place at the Kellogg School of Management, Northwestern University and in 2018 and in 2019 it was held at the Volatility Institute of NYU Shanghai.
The editorial board for these annual series is made up of professors as follows:
Torben G. Andersen (Northwestern)
Luc Bauwens (Catholic University of Louvain)
Francis X. Diebold (University of Pennsylvania, past President of SoFiE)
Eric Ghysels (University of North Carolina, Chapel Hill, Secretary and Founding Co-President of SoFiE)
Ravi Jagannathan (Northwestern and President SoFiE)
Per Mykland (University of Chicago and President-Elect SoFiE)
Eric Renault (Brown University and past SoFiE President)
Neil Shephard (Harvard University)
Viktor Todorov (Northwestern)
The SoFiE Financial Econometrics Summer School 2020 is to be held at the Volatility Institute, NYU Shanghai, from Monday July 20 through Friday July 24, 2020
The course is intended for Ph.D. students and researchers in statistics, econometrics and finance. The course assumes familiarity with basic regression analysis, principles of univariate and multivariate time series analysis as well as basic models of volatility but is otherwise self-contained.
In this Summer School, Professors Babii and Ghysels will present research that focuses on Mixed data sampling (MIDAS) regression models and filtering methods with applications in finance and other fields. MIDAS regressions can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data. "Big" is in parenthesis in the title because the lecture series will start with MIDAS for conventional data sets and then cover mixed frequency data analysis using machine learning and other large dimensional data econometric techniques.
This course is open to all students and researchers who apply to attend and are admitted. The course will offer a limited number of course participants an opportunity to present their current research and receive feedback from the instructors and other course participants. Students interested in making a presentation (which is entirely optional) should indicate so on their application and submit the research paper that will form the basis of their presentation. Students who are selected to make a presentation will be informed at the same time as they receive their admission decisions.
Students will be provided with a packet of lecture notes when the course starts.
Professor Eric Ghysels (University of North Carolina at Chapel Hill)
Eric Ghysels is the Edward M. Bernstein Distinguished Professor of Economics at the University of North Carolina at Chapel Hill and Professor of Finance at the Kenan-Flagler Business School. He obtained his Ph.D. from the Kellogg Graduate School of Management at Northwestern University. He has been a visiting professor or scholar at several major U.S., European and Asian universities. He served on the editorial boards of several academic journals and was co-editor of the Journal of Business and Economic Statistics and editor of the Journal of Financial Econometrics. He has published in the leading economics, finance and statistics journals and has published several books. He is also the Founding Co-President of the Society for Financial Econometrics (SoFiE). He was a Resident Scholar at the Federal Reserve Bank of New York during the 2008-2009 financial crisis and a Duisenberg Fellow at the European Central Bank in 2011 during the sovereign debt crisis. He is a Fellow of the American Statistical Association, Fellow of the Journal of Econometrics, Fellow of the Society for Financial Econometrics and holds a Honorary Doctorate from HEC Liege. He is currently co-editor of the Journal of Applied Econometrics and Faculty Research Director of the Rethinc.Labs at the Kenan Institute. His most recent research focuses on MIDAS (meaning Mi(xed) Da(ta) S(ampling)) regression models and related econometric methods, machine learning, artificial intelligence, big data, FinTech, and quantum computing applications in finance.
Professor Andrii Babii (University of North Carolina at Chapel Hill)
Andrii Babii is an Assistant Professor of Economics at the University of North Carolina at Chapel Hill. He obtained his Ph.D. from the Toulouse School of Economics in France. He has published in leading econometrics journals. His most recent research focuses on machine learning and big data analysis in econometrics, causal inferences, nonparametric and high-dimensional statistics. He was a recipient of several scholarships and awards, including the Jean-Jacques Laffont Scholarship and the Jae-Yeong Song and Chunuk Park Teaching Award. He is also the alumni of the 2013 SoFiE Summer School at Oxford University and the 2014 SoFiE Summer School at Harvard University.
To be determined
Monday July 20
11:45 - 13:20 Registration
13:20 - 13:30 Welcome/Introduction
13:40 - 16:40 Lecture 1: Introduction to MIDAS Regressions
18:30 - Welcome reception
Tuesday July 21
9:00 - 12:00 Lecture 2: Kalman Filter, Mixed Frequency Data and Nowcasting
13:30-15:00 Lecture 3: Vector Autoregressive Models with Mixed Frequency Data
15:30-17:00 Lecture 4: MIDAS Volatility Models, Correlation Models and Quantile regressions
18:00 - Dinner
Wednesday July 22
9:00 - 12:00 Lecture 5: Practical applications
12:00 - 13:30 Lunch
13:30 - 15:00 Student presentations
15:00 - 15:30 Break
15:30 - 17:00 Student presentations
17:00 - 18:00 Invited speaker (TBD)
Thursday July 23
9:00 - 12:00 Lecture 6: Forecasting and empirical risk minimization
13:30-15:00 Lecture 7: Regularized regressions
15:30-17:00 Lecture 8: Machine learning with mixed frequency data
Friday July 24
9:00 - 12:00 Lecture 9: Factor models with mixed frequency data
Applicants should register and submit electronical materials through the following registration website:
The applications should include a full CV and motivation letter (half-page length) explaining why attending this course would be helpful to the applicant’s research work. All materials should be in pdf version. The application deadline is 15 April 2020. Decisions will be emailed out by 30 April 2020.
Applicants are encouraged to present some of their thesis work during the morning session of the last day (Friday). For this, they should preferably append a paper to their application. They can submit an extensive abstract if the paper is not yet finished. The paper topics need not be closely linked to the course but obviously must be in the field of financial econometrics. Papers will be selected by the organizing committee on the basis of their quality.
$300 for Ph. D. students and faculty members attending this course.
$600 for Ph.D. level colleagues from other institutions.
Confirmed admission of a selected applicants will be conditional on the fee payment in due time (details will be provided in the admission email).
All accepted participants will be expected to be members of the Society for Financial Econometrics or join before their place is confirmed.
See http://sofie.stern.nyu.edu/membership on how to join the society (where a student membership option is available).
Travel Accommodation Costs:
Attendees will be required to pay their own travel and accommodation. No assistance will be offered in this respect. During the teaching schedule (Monday-Friday) at NYU Shanghai, lunch, coffee and tea will be provided free of charge. Evening meals will not be organized and will be at the expense of the participants.