This academic track provides expert training in digital economics and methods for analyzing mass data. Harnessing this type of data requires new skills to be able to process high volumes of input and extract useful information. This track therefore aims to train quantitative economists in processing and modeling large, complex datasets to shed light on the decisions of businesses and institutional stakeholders. Employment opportunities are highly varied: data analyst, consultant, economic expert, etc.
Skills acquired:
ECTS : 3
ECTS : 6
Description du contenu de l'enseignement :
While this is a fairly recent technology, this class will take students through the fundamentals of blockchains as well as implications regarding financial, economic or social interactions. The class will start by some history needed to understand what lead to the creation of Bitcoin, the first blockchain, in 2009. We will then review the detailed functioning of a blockchain. We will continue by discussing important current developments in the industry as well as implications for the economic environment. Lastly, we will discuss potential future developments and how blockchains will impact a broad range of industries. Students will also be introduced to recent academic work related to blockchains.
Students will be asked to pick a blockchain project from a list and present it briefly during the presentation session in front of the class (group presentation). When reaching this presentation session, students will be expected to be able to assess the pros and cons of a given blockchain project, and have a critical opinion on this project.
Compétence à acquérir :
The objective of this class is to give students a deep theoretical overview of what a blockchain is. Nonetheless we will also use mock-blockchains, write smart contracts and interact with them, through some computer sessions. This will help solidify the knowledge learned and de-mystify the functioning of a blockchain.
Students will gain a deep knowledge of how a blockchain works internally. They will also be very aware of the different issues and perhaps they will be able to spot new use cases for a blockchain.
Mode de contrôle des connaissances :
The evaluation is composed of a group presentation (1/3), homework (1/3) and a final exam (1/3). Class participation can be highly rewarded especially for students who struggle with homework. Students are encouraged to actively interact during the class.
Bibliographie, lectures recommandées :
Melanie Swan, Blockchain: Blueprint for a new economy, O’Reilly, 2015
Andreas Antonopoulos, Mastering Bitcoin, 2nd edition, O’Reilly, 2017
Andreas Antonopoulos / Gavin Wood, Mastering Ethereum, 1st edition, O’Reilly, 2018
Primavera De Filippi/ Aaron Wright, Blockchain and the Law: The Rule of Code, Harvard University Press, 2018
ECTS : 0
Description du contenu de l'enseignement :
The lecture is a sequence of use-cases in the industry and in the service business performed by professionals.
Compétence à acquérir :
Knowing some of the most common use cases of data sciences in firms decision making business.
Mode de contrôle des connaissances :
Presentation of a resume of the lectures before a jury.
ECTS : 3
ECTS : 3
ECTS : 3
Description du contenu de l'enseignement :
The course will equip students with the necessary knowledge to be able to undertake econometric analysis of the type commonly associated with modern financial econometrics research. Substantial emphasis will be placed on the development of programming skills in Python (or in MATLAB, especially for financial contagion and multivariate analysis).
Course outline:
Compétence à acquérir :
The course provides a deep knowledge of the advanced time series techniques and their application to systemic risk. A technical presentation of these models will be given, before studying applications of these models to systemic risk.
Mode de contrôle des connaissances :
Individual homework assignment.
Bibliographie, lectures recommandées :
Benoit, S., Colliard, J.-E., Hurlin, C. and C. Pérignon (2017) Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, 21(1), 109-152.
Benoit, S., Hurlin, C. and C. Pérignon (2019) Pitfalls in Systemic-Risk Scoring, Journal of Financial Intermediation, 38, 19-44.
Campbell, S. D. (2004) A Review of Backtesting and Backtesting Procedures, Working paper, Federal Reserve Board.
Christofferson, P. and Pelletier, D. (2004) Backtesting Value-at-Risk: A Duration-Based Approach, Journal of Financial Econometrics, 2(1), 84-108.
Du, Z. and J. C. Escanciano (2015) Backtesting Expected Shortfall: Accounting for Tail Risk, Management Science.
Diebold, F.X. and K. Yılmaz (2009) Measuring Financial Asset Returns and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(1), 158-171.
Diebold, F.X. and K. Yılmaz (2012) Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers, International Journal of Forecasting, 28(1), 57-66.
ECTS : 3
ECTS : 6
Description du contenu de l'enseignement :
Theory and practice of competition in network industries; antitrust issues; theory of network and network effects; two-sided platforms and pricing
Compétence à acquérir :
Understanding of competition and regulation issues in network and digital economics
Mode de contrôle des connaissances :
Written exam
Bibliographie, lectures recommandées :
Belleflamme-Peitz, Industrial Organization
ECTS : 12
Description du contenu de l'enseignement :
Students must intern at a company (semester 4) for at least 4 months.
Compétence à acquérir :
The students should also complete an end-of-studies internship lasting at least 4 months. The curriculum includes guest lectures by visiting professionals on issues related to Big Data, providing another means of connecting with relevant business circles.
Mode de contrôle des connaissances :
The internship will conclude with a report to be reviewed by a committee.
ECTS : 9
Description du contenu de l'enseignement :
The course gives a thorough presentation of the machine learning field and follows this outline:
Compétence à acquérir :
After attending the course the students will
Mode de contrôle des connaissances :
ECTS : 3
Description du contenu de l'enseignement :
Economic science has evolved over several decades toward greater emphasis on empirical work. Ever increasing mass of available data (’big data’) in the past decade is likely to have a further and profound effect on economic research (Einav and Levin, 2014). Beyond economic research, governments and the industry are also increasingly seeking to use ’big data’ to solve a variety of problems, usually making use of the toolbox from machine learning (ML).
The question we ask in this course is the following : What do we (not) learn from big data and ML as economists? Is ML merely applying standard techniques to novel and large datasets? If ML is a fundamentally new empirical tool, how does it fit with what we know? In particular, how does it fit with our tools for causal inference problems? As empirical economists, how can we use big data and ML? We’ll discuss in detail how ML is useful to collect new data, for prediction in policy, and to provide new tools for estimation and inference.
Compétence à acquérir :
Course objectives:
1. Present a way of thinking about ML that gives it its own place in the econometric toolbox.
2 Develop an intuition of the problems to which it can be applied in economics, and its limitations.
3 Data challenge in health policy.
Mode de contrôle des connaissances :
Grading:
1. In-class pairwise presentation of an academic paper (20% of overall grade).
2. Data challenge project : written report + in-class presentation (80% of overall grade).
Bibliographie, lectures recommandées :
ECTS : 3
ECTS : 3
ECTS : 3
ECTS : 0
ECTS : 3
Description du contenu de l'enseignement :
This course introduces all major uses cases of the blockchain industry from a technical perspective. The course begins with an introduction of Github and Solidity coding fundamentals before diving into smart contract development. Participants will learn the most common ERC standards for tokens and NFTs before building more complex contracts for DAOs. Finally, a deep dive into the EVM and an outlook into the future of Blockchain - L2s.
The course schedule is as follows:
Lecture 1 - Blockchain Basics and Development
Lecture 2 - Solidity Fundamentals
Lecture 3 - Contracts and Complex Data Structures
Lecture 4 - ERC20 Tokens and Tokenomics
Lecture 5 - Intro to DeFi
Lecture 6 - Further DeFi Applications
Lecture 7 - NFTs
Lecture 8 - ReFi and NFT applications (Guest Lecture)
Lecture 9 - SDLC, Security and Testing
Lecture 10 - DAOs and Governance
Lecture 11 - Assembly and Gas Optimization
Lecture 12 - Scaling the future of Ethereum: L2s
Compétence à acquérir :
At the conclusion of this course, participants will gain a solid foundation of Solidity programming and smart contract development, enough to be considered a junior blockchain developer. Participants will also gain an understanding of the open source philosophy and collaboration style.
Mode de contrôle des connaissances :
The level of mastery will be continuously assessed throughout the course by:
Bibliographie, lectures recommandées :
Mastering Ethereum by Andreas Antonopoulos - https://github.com/ethereumbook/ethereumbook
ECTS : 6
Description du contenu de l'enseignement :
This lecture is thought as an introduction to the analysis of complex data, and particularly to that
having a temporal component. Methods aimed at exploring and modelling time series, longitudinal data
and graphs with temporal components will be addressed. The issues of detecting patterns, breakpoints, changes of regimes, and
anomalies will be at the core of the different approaches.
The first chapters will be devoted to hidden Markov models. After having briefly recalled some definitions
and properties of Markov processes, we will define hidden Markov processes, illustrate them with several
examples and give some of their properties. Inference techniques using the EM algorithm and Bayesian
approaches will be presented and illustrated in practice. We will particularly focus on some specific
models which are extremely useful for segmenting time series stemming from the economics field, such as
autoregressive Markov switching models.
The second part of the lecture will tackle the issue of change-point detection methods. We will start
by introducing the change-point detection issue. More specifically, we will consider several frameworks and derive inference
procedures for computing and locating change-points : online vs. offline strategies, single vs. multiple
change point detection, known vs. unknown number of change points, parametric vs. non-parametric
approaches.
The third chapter will be aimed at introducing the issue of anomaly detection in the context of temporal
data. After having defined what an anomaly is, we will start by assessing whether and how hidden-Markov
models and change-point analysis may be useful for detecting anomalies. Then, we will compare these
two approaches with other techniques, stemming either from the field of computational statistics, or from
that of machine learning. During this chapter, we will also consider the questions of detecting patterns
and clustering temporal data.
The fourth chapter will address data that can be modelled as a graph or a temporal graph. We will start
by introduce some definitions and summaries for characterising the network (degree distribution, centrality
indices, ...). Afterwards, we will tackle the questions of community detection and graph clustering.
Eventually, we will address the issues of random networks and associated tests for randomness.
Models and methods introduced in this lecture will be practiced using existing implementations in R and
“real-life” datasets.
Compétence à acquérir :
Gain some background and perspective of time series analysis from a data science point of view.
Be able to handle temporal data subject to anomalies and change-points.
Have some basic knowledge about graphs and temporal graphs mining.
Mode de contrôle des connaissances :
Data challenge. A project to be done individually or by two, analysing real life data.