Network Analysis Techniques For Applications in Finance and Economics
Registration deadline: 12 May 2020
Over the last years, network analysis has become an active topic of research, with numerous applications in macroeconomics and finance. In a nutshell, network analysis is concerned with representing the interconnections of a large panel as a graph: the vertices of the graph represent the variables in the panel, and the presence of an edge between two vertices denotes the presence of some appropriate measure of dependence between the two variables. Dependence can derive from direct exposures or from indirect or common exposures.
From an economic perspective, the interest on networks has been boosted by the research of, inter alia, Acemoglu et al. (2012), which shows that individual entities can have a non-negligible effect on the aggregate behavior of the economy when the system has a high degree of interconnectedness. Especially since the 2008 global financial crisis, the interest in analyzing the role of network structure in transmitting – or dissipating – stress has grown significantly. This work is concerned with the theory and practice network analysis techniques for applications in finance and economics.
- Network models for large panels of economic and financial time series
- Estimation of large dimensional network models
- Direct and indirect contagion
- Empirical illustrations
Meet the instructors
Christian Brownlees is an Associate Professor in the Department of Economics and Business at the Universitat Pompeu Fabra. Christian received his PhD in Statistics and B.S. in Economics and Quantitative Methods from Universita’ di Firenze. He was also a visiting PhD researcher at UCSD and post-doc researcher at NYU. Christian’s research lays at the intersection of statistics, econometrics, economics and finance. In particular, his research focuses on volatility and systemic risk. Christian has published in the Journal of Econometrics, the Review of Economics and Statistics, Annals of Statistics and the Review of Financial Studies.
Iman van Lelyveld is a Senior Policy Advisor with DNB’s Statistics Division and Professor of Banking and Financial Markets at the Finance Group of the VU Amsterdam. At DNB he is spearheading the Data Science Hub initiative. He has published widely on international banking and financial networks. He has worked for Deutsche Bank, the Bank of England, and the International Data Hub at the Bank for International Settlements (BIS). At the BIS he helped to setup analysis of the exposure network of the largest banks in the world.
A Master’s degree in Economic and/or Finance is required to attend the course.
To be able to follow the course, a knowledge and understanding of Time Series Econometrics equivalent to a graduate-level course is recommended.
1750€ – Public Authorities (e.g. National Competent Authorities, Central Banks and European Institutions).
1900€ – Private Sector.
950€ – Academics (Full-time Professors, full-time PhD Students and full-time Research Associates). Please submit a certificate attesting your status of Professor, PhD Student or Research Associate to email@example.com before registering. FBF secretariat will provide you with a code to register.
The course fee covers coffee and lunch breaks. Travel and hotel costs are not included.
Please note that the payment must be settled two weeks before the start of the course.
- In case a course is cancelled, registered participants will receive the full refund.
- In case a course is moved to another date, registered participants may request a voucher to attend another FBF course.
- Registered participants who have not yet paid the registration fee can cancel their participation until one month before the start of the course.
- The registration fee is non-refundable, however it will be possible to transfer registration to another person or request a voucher for another FBF course up to 20 days before the start date of the course.
For more details, please contact firstname.lastname@example.org
A certificate of attendance will be provided to all participants after the course.
Please notice that the course dinner, and most of the social activities, will take place downtown.
Recommended hotels in downtown Florence:
Recommended hotels nearby the EUI:
Suggested restaurants in Florence city centre
- Coquinarius – Ph. +39 055 230 21 53
- SimBIOsi – Ph. +39 055 064 01 15
- Restaurant Accademia – Ph. +39 055 21 73 43
- Restaurant Cucina Torcicoda – Ph. +39 055 265 43 29
- Finisterrae – Ph. +39 055 263 86 75
- Il Vezzo – Ph. +39 055 28 10 96
- Osteria di Giovanni – Ph. + 39 055 28 48 97
On arrival, participants will be provided with temporary wi-fi access for the whole duration of the course.
General information on local transport
From Florence airport:
Florence airport is located 8 km from the city centre, approximately 30 minutes by taxi or bus. Taxis can be found outside the arrivals terminal; no reservation is needed. A taxi ride from the airport costs about €20 and takes approximately 25/30 minutes.
A tramway (line T2) connects the airport to the city centre. Trains leave from the airport terminal and take 20 minutes to the main railway station. One-way tickets can be bought from vending machines for €1.50.
The airport is also connected to the main railway station in Florence by a shuttle bus (‘Vola in bus’) that leaves every 30 minutes (on the hour and on the half-hour) and takes 25 minutes. Tickets are available on board for €6.00.
From the central railway station:
Bus tickets are sold outside the railway station, at ATAF ticket kiosks and vending machines, tobacconists (tabacchi), newspaper kiosks (edicole), and most cafès (bar). Bus tickets can be purchased also on board with a contactless credit card (Mastercard, Maestro, Visa and V PAY).
From the A1 Milano-Napoli (Autostrada del Sole), take the Firenze Sud exit and follow directions to the city centre/Stadio. Follow the directions to the stadium (Stadio), then for Fiesole. San Domenico is on the main road to Fiesole.
The EUI has several free parking areas available all over the Campus.