This course explores econometric methods for monitoring and forecasting macroeconomic and financial risks, conditional upon current and past indicators of economic and financial conditions. Initially, the course introduces the Growth-at-Risk framework, which uses quantile regression to gauge downside risks by projecting GDP growth based on these indicators. It covers all steps of operationalization, including interpolation from quantiles to densities, measurement of shortfall, out-of-sample evaluation of density forecasts with a focus on real-time performance, and forecast combination. Subsequently, we cover other target variables including the general outlook at risk, inflation at risk, employment at risk, housing at risk, and stock market at risk, as well as other methods including distributional regression, nonparametric density estimation, mean-variance regression, and Markov-switching models. Practical applications relevant to the US and the Euro Area will be developed and discussed using MATLAB.

The course concludes with a workshop where leading scholars in the fields of macroeconomics, finance, and data science will present their ongoing work on monitoring and forecasting risk. Presenters/discussants include: Nina Boyarchenko (New York Fed), Gianluca Bontempi (ULB), Christian Brownlees (UPF and BSE), Christophe Croux (KULeuven), Christine De Mol (ULB), Catherine Doz (Paris School of Economics), Fernando Duarte (Brown University), Gary Koop (University of Strathclyde), Michele Lenza (European Central Bank), Francesca Loria (Federal Reserve Board), Haroon Mumtaz (​​Queen Mary, University of London), Emanuel Moench (Frankfurt School of Finance & Management), Francesca Monti (UCLouvain), Ivan Petrella (Warwick Business School), Jean-Paul Renne (HEC Lausanne), Andrej Sokol (Bloomberg LP), and Dick Van Dijk (Erasmus University Rotterdam).

The audience includes graduate students, academics, practitioners, and policymakers. Apply here. 

To participate, please submit your application to Prof. Leonardo Iania at  leonardo.iania@uclouvain.be  by the 14th of July, with the words “SoFiE Summer School 2024’’ in the subject box. Decisions will be emailed out by the 20th of July 2024. The applications should include a CV and, in the text of the email, a brief motivation on why you would like to attend this course. 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. There will be both short presentations and poster sessions. Students interested in making a presentation/poster (which is optional) should indicate so on their application and submit a draft of the research paper that they wish to promote. Students who are selected to make a presentation/poster will be informed at the same time as they receive their admission decisions. 

Fees: 400 Euro for (full-time) Ph.D. students, 600 for (full time) academics (Post-docs, Profs, etc.) and 1100 Euro for others. All accepted participants are expected to be members of the Society for Financial Econometrics or to join before their place is confirmed. The course is free for the members of the organising institutions. Further info on how to join SoFiE is available at http://sofie.stern.nyu.edu/membership (where a student membership option is available). Fees cover the inscription costs, lunches, and coffee breaks foreseen in the program. Confirmation of admission of selected applicants is conditional on receipt of the fee payment in due time (details to be provided in the admission email).