• Course title: 3.PAT1.Introduction to Private Assets
• Number of ECTS: 6
• Course code: MScFE_PA-1
• Module(s): Module 3.PAT: Specialisation – Private Assets Track
• Language: EN
• Mandatory: Yes

Understand the Private Assets Ecosystem: Gain a comprehensive overview of the private assets landscape and Luxembourg’s pivotal role in it.

Identify Key Players and Stakeholders: Recognize the roles and responsibilities of general and limited partners, as well as other key stakeholders.

Navigate the Investment Lifecycle: Understand the stages of private assets investments, from fundraising to exits.

Analyze Investment Styles and Trends: Evaluate different asset and investment styles and understand current trends such as digital transformation and ESG integration.

Comprehend Legal and Regulatory Frameworks: Understand the legal structures and compliance requirements for private assets funds, particularly in Luxembourg.

Evaluate Fund Performance: Assess fund performance and make informed decisions regarding fund selection.

Understand Cross-Border Transactions: Navigate international regulations and manage cross-border transactions effectively.

Part 1: Introduction to Private Assets

The first part provides an overview of the Private Assets ecosystem, highlighting Luxembourg’s significant role in the global private assets landscape. Participants will explore various asset and investment styles to understand the diversity within private assets. The lifecycle of private assets investments will be covered, including the stages of fundraising, deal sourcing, value creation, and exits. The course will introduce the key players and stakeholders in private assets, such as general and limited partners, explaining their roles and interactions. We will discuss the reasons for investing in private assets and identify the typical investors in this asset class. Current trends in private assets, including democratization, digital transformation and the integration of Environmental, Social, and Governance (ESG) factors, will be examined.

Part 2: The Fund Side

The second part delves into the legal structures for private assets funds, including contractual funds, limited partnerships, Special Purpose Vehicles (SPVs), and holding companies in Luxembourg and other key jurisdictions. Participants will learn about compliance with Luxembourg’s legal framework for private asset management, including essential legal documents, passports under the Alternative Investment Fund Managers (AIFM) directive, and marketing documents such as the Packaged Retail and Insurance-based Investment Products Key Information Document (PRIIPs KID). The module will cover cross-border transactions and the relevant international regulations that impact private assets. The investment lifecycle from the fund’s perspective will be detailed, providing insights into the various stages and processes involved. We will also discuss the roles of other key players in the private assets’ ecosystem, including custodian banks, administrators, lawyers, and auditors. The module will conclude with an examination of the role of the regulator in overseeing private assets funds and ensuring compliance with legal and regulatory requirements.

Assessment Modality
End-of-course assessment
Assessment Tasks
Type of Assessment
Grading Scheme
Weight for final Grade
Task 1
Oral exam
 
0-20
100%
Specific Assessment Rules
20 minutes closed book
 

Literature 
Will be provided during the first lecture

• Course title: 3.E2.Household Finance and Real Estate
• Number of ECTS: 5
• Course code: MScFE_BK-7
• Module(s): Module 3.PAT: Specialisation – Private Assets Track
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to :Carry out their original empirical work, do the critical reading of pertinent articles related to the question and know how to handle complex survey data.

• Carry out their original empirical work, critically read pertinent articles related to the research question and know how to handle real estate and complex survey data.


• Critically assess the individual and social benefits of important financial products accessible to consumers.


• Approach consumer financial markets with more empathy towards potential customers and design better functioning products and markets.


• 
  Familiarise themselves with real-world experience of household acquiring their own residence, financial savings, debt and investments, up to current developments.
  

The course also equips students with a good understanding of commercial real estate market fundamentals across different property sectors. Students will become familiar with key valuation methodologies for property assessment, gain a comprehensive view of the real estate investment lifecycle from acquisition to disposition, and critically assess the opportunities and risks of various real estate fund vehicles and investment strategies employed by institutional investors.

The objective of the course is to understand how households use financial instruments to attain their objectives, with an emphasis on real estate investment. First, we review the empirical facts on household wealth and inequality, as well as the rational and behavioural aspects underlying consumer financial decision-making. We then study current household financial products and the competitive landscape in credit, investment, and advisory markets. A particular emphasis is placed on residential real estate as both a key asset and a source of debt for households, highlighting its role in wealth accumulation, risk exposure, and financial vulnerability. We also cover innovations in consumer financial products and the regulation of household finance, and provide an overview of recent research on residential real estate markets, including price dynamics, housing affordability, and the impact of macroeconomic and regulatory shocks.
The course includes a group project in which students apply the concepts covered in class and deliver a presentation. We introduce two key data sources: one on household finance and one on residential real estate. These datasets are discussed in class and Stata tutorials guide students on how to prepare their dataset for their own project and how to apply basic estimation techniques on household surveys or on real-estate data collected from online advertisements.
The commercial real estate module explores the main commercial real estate (CRE) categories, core investment fundamentals and key real estate fund management practices. Students will build a foundation of the CRE sector, navigate the investment process, and gain insight into how institutional investors manage real estate funds. The course is designed to bridge academic theory with real-world application, reflecting current market realities and the decision-making frameworks used by investment firms and fund managers. With its industry-oriented approach, the curriculum equips students for careers in real estate asset management, fund management, and related fields by emphasizing the skills and perspectives most valued by employers in the sector.

• Oral exam (45%)


• Presentation (45%)


• Active participation (10%)

A comprehensive reading list of recent academic publications, working papers and market reports, is available from the instructors.

• Course title: 3.PAT2. Buyouts & Venture Capital
• Number of ECTS: 6
• Course code: MScFE_PA-2
• Module(s): Module 3.PAT: Specialisation – Private Assets Track
• Language: EN
• Mandatory: Yes

On completion of the course unit successful students will be able to: 

• Understand Leveraged Buyouts: Gain a comprehensive understanding of the financing structures and strategies used in leveraged buyouts.


• Understand how to setting up buyout funds.


• Evaluate (buyout) fund performance.


• Adress corporate governance problems in leveraged buyouts.


• Develop financing structures for buyout funds. 


• Create Operational Value: Identify and implement techniques for cost reduction, efficiency improvements, and growth acceleration in acquired companies.


• Conduct Due Diligence: Perform thorough due diligence and manage post-acquisition integration effectively.


• Evaluate Early-Stage Investments: Source early-stage investment opportunities and apply startup valuation techniques.


• Structure Venture Capital Deals: Develop term sheets, equity splits, and governance frameworks for venture capital deals.


• Develop Growth Strategies: Formulate growth strategies for startups and monitor the performance of venture capital portfolios.


• Plan Exit Strategies: Understand and execute various exit strategies, including IPOs, mergers, and acquisitions.
  

Part 1: Buyouts
The first part will cover leveraged buyouts, focusing on the various financing structures and strategies used to acquire companies. In a first part, the role of leverage and its relation to betting against beta is laid out. Next, individual companies are analyzed and participants will learn about operational value creation, including techniques for cost reduction, efficiency improvements, and growth acceleration within acquired companies. Finally, the structure of buyout funds will be analyzed. The course will also delve into the due diligence process and the critical steps involved in post-acquisition integration to ensure successful buyouts.
Part 2: Venture Capital
The second part will explore early-stage investments, including how to source opportunities and the techniques used for startup valuation. Participants will learn how to structure venture capital deals, including the creation of term sheets, equity splits, and governance frameworks. The course will cover growth strategies for startups and how to monitor the performance of a venture capital portfolio. Finally, participants will examine various exit strategies, such as Initial Public Offerings (IPOs), mergers, and acquisitions.

60% written exam (2h, closed book)

30% presentation

10% active participation

Literature will be provided in class

• Course title: 3.PAT3. Infrastructure and Private Debt
• Number of ECTS: 3
• Course code: MScFE_PA-3
• Module(s): Module 3.PAT: Specialisation – Private Assets Track
• Language: EN
• Mandatory: Yes

On completion of the course unit successful students will be able to : 

• Understand Market Trends: Gain insights into current and emerging trends in the Infrastructure and Private Debt markets.


• Apply Valuation Techniques: Utilize various valuation techniques to assess the value of Infrastructure and Private Debt investments.


• Navigate Regulatory Requirements: Understand the regulatory requirements for Infrastructure and Private Debt investments in Luxembourg and the EU.


• Finance Infrastructure Projects: Learn about structures and strategies used to finance large-scale infrastructure projects as well as sustainability aspects related to Private Assets.Implement Risk-Sharing Mechanisms: Apply risk-sharing mechanisms between public and private entities in infrastructure projects.


• Incorporate ESG Considerations: Integrate ESG considerations into infrastructure development projects. Reporting requirements and operational execution relating to ESG compliance.


• Understand Private Debt Instruments: Gain a comprehensive understanding of direct lending, mezzanine financing, and syndicated loans.


• Assess Credit Risk: Learn how to assess credit risk and price illiquid debt instruments.


• Navigate Legal Frameworks: Understand the legal and regulatory frameworks governing private debt investments.

Part 1: Infrastructure
This part will focus on financing large-scale infrastructure projects, discussing both debt and equity structures used to fund these projects. Participants will learn about risk-sharing mechanisms between publicthe types and private entities, which are crucial for the successful executioncharacteristics of infrastructure assets as well as the structures and strategies to finance infrastructure projects. This part will also cover Environmental, Social, and Governance (ESG) and sustainability considerations in infrastructure development, highlighting the importance of sustainable and responsible investment practices. The part will conclude with an overview of sustainable finance and impact investing in Private Assets.
Part 2: Private Debt
This part will explore various forms of private debt, including direct lending, mezzanine financing, and syndicated loans, providing a comprehensive understanding of these financing options. It will put these financing tools in context to bank loans and the requirements to finance corporates in growth and restructuring situations. Participants will learn how to assess credit risk and price illiquid debt instruments, which are essential skills for managing private debt investments. The role of private debt funds in alternative financing markets will be discussed, highlighting their importance in providing capital to businesses that may not have access to traditional financing. This part will also cover the legal and regulatory frameworks governing private debt investments, ensuring participants are aware of the compliance requirements in this sector.
Based on the general understanding of Private Debt as a financing instrument, participants will access the stages of initiation of a debtor-lender relation, monitoring during the investment period and exit of the investment.

40% Written exam (1h, closed book)

40% Case study

20% Active participation

Literature will be provided during the first lecture

• Course title: 3.PAT4. Valuation, risk, liquidity and taxation
• Number of ECTS: 5
• Course code: MScFE_PA-4
• Module(s): Module 3.PAT: Specialisation – Private Assets Track
• Language: EN
• Mandatory: Yes

    Identify and Manage Risks: Recognize and manage risks associated with illiquid and alternative assets.
    Understand Risk Factors: Identify specific risk factors for different investment styles within private assets.
    Conduct Stress Testing: Apply stress testing and scenario analysis techniques to evaluate portfolio resilience.
    Diversify Portfolios: Develop portfolio diversification strategies to mitigate risk and enhance stability.
    Apply Valuation Methods: Utilize various valuation methods and understand the challenges in illiquid markets.
    Implement the Three Lines of Defense Model: Apply the three lines of defense model in risk management for private assets.
    Structure Tax Efficiently: Develop effective tax structuring strategies for private assets funds in Luxembourg.
    Understand Tax Considerations: Recognize key tax considerations for limited partners and general partners.
    Navigate International Tax Treaties: Understand the implications of international tax treaties and OECD guidelines on private assets investments.

Part 1: Valuation, Risk, and Liquidity Management of Private Assets

The first part will cover the identification and management of risks associated with illiquid and alternative assets, providing participants with strategies to mitigate these risks effectively. Participants will learn about the specific risk factors associated with different investment styles within private assets. This part will include stress testing and scenario analysis techniques for private asset portfolios, helping participants understand how to evaluate portfolio resilience under various conditions. Portfolio diversification strategies will be discussed to help mitigate risk and enhance portfolio stability. Participants will explore various valuation methods, and the challenges faced in illiquid markets, gaining insights into accurate asset valuation. This part will also introduce the three lines of defense model, explaining its application in risk management for private assets.

Part 2: Taxation Aspects of Private Assets

The second part will focus on taxation of private assets funds in Luxembourg, providing participants with knowledge of key tax aspects of private assets fund structures. Key tax considerations for limited partners and general partners will be examined, ensuring participants understand the tax implications for different stakeholders. This part will also cover the implications of international tax treaties and OECD guidelines, highlighting the global tax landscape and its impact on private assets investments.

Assessment Modality
End-of-course assessment
Assessment Tasks
Type of Assessment
Grading Scheme
Weight for final Grade
Task 1
Written exam
 
0-20
100%
Specific Assessment Rules
2h, closed book
 

Literature: Will be provided during the first lecture.

• Course title: 3.E1.Behavioural Finance
• Number of ECTS: 5
• Course code: MScFE_BK-6
• Module(s): Module 3.E: Special Topics in Finance & Economics – Electives II
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to :

• Know anomalies in finance and economics and understand theoretical explanations


• Know the foundations of behavioural finance and behavioural economics


• Understand human behaviour

 The course:

• introduces to the literature on the anomalies in financial markets and the theories of behavioral economics and finance. Departing from the standard paradigm in financial economics, expected utility and the capital asset pricing model, we pinpoint anomalies that show up in the data from the real world and the laboratory.
• includes models of prospect theory, noise trader risks, psychological game theory, bounded rationality that help to understand the divergence between observed behavior and the standard paradigm of financial economics.

Written exam (2 hours)

Literature:

 

• Barberis and Thaler 2003. “A survey of behavioral finance.” Handbook of the Economics of Finance, 1, 1053-1128


• Dhami, Sanjit, 2016, Foundations of behavioral economic analysis. Oxford University Press


• Gigerenzer and Selten (Eds.) 2002. Bounded rationality: The adaptive toolbox. MIT press


• Hens, Thorsten and Kremena Bachmann, 2009, Behavioral Finance for Private Banking, Wiley


• Kahneman and Tversky 1979 “Prospect Theory: An Analysis of Decision under Risk” Econometrica


• Shleifer Andrei, 2001, Inefficient markets – An introduction to behavioral finance. Calderon Lectures in Economics


• Thaler 1985 “Mental accounting and consumer choice”. Marketing science, 4(3), 199-214


• Thaler 1999 “Mental accounting matters”. Journal of Behavioral decision making, 12(3), 183-206


• Thaler and Johnson 1990 “Gambling with the house money and trying to break even”. Management science 36, 643-660


• Tversky and Kahneman 1992 “Advances in prospect theory”. Journal of Risk and uncertainty, 5(4), 297-323

Further readings will be communicated during the lectures.

• Course title: 3.E3.Financial Engineering
• Number of ECTS: 5
• Course code: MScFE_BK-8
• Module(s): Module 3.E: Special Topics in Finance & Economics – Electives II
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to :

• Show proficiency in probability and statistics, calculus, programming and use these tools to model markets and drive decision making


• Understand risk and analyze financial data


• Design and implement complex financial models that allow financial firms to price and trade securities


• Understand the current academic and practitioner literature on financial engineering


• Get exposed to some of the most applicable machine learning techniques in finance considering that machine learning and Artificial Intelligence (AI) play a significant role in the creation of models

Understanding risk and analysing data to drive policy and decision making is the name of the game in institutions like i.e. banks, insurance companies, hedge funds, and governments.Financial Engineering is the study of applying math, statistics, computer science, economic theory, and other quantitative methods to analysing and modelling financial markets. Financial engineers work at the intersection between data science and finance. The first financial engineers were Fischer Black, Robert Merton, and Myron Scholes, infamous for their options pricing model known as the Black-Scholes Model. This model won the Nobel prize in economics and is the foundation for the explosion in derivative markets. 

The final grade of the course will be derived from the grade for participation (25%), the presentation (25%) and the grade for the research paper (50%).

Literature:

 
“Statistics and Data Analysis for Financial Engineering”, 2nd edition by David Ruppert and David
S. Matteson, Springer, ISBN 978-1-4939-2614-5 (eBook)
 
Research papers

• Course title: 3.E4.Professional seminars
• Number of ECTS: 5
• Course code: MScFE_BK-9
• Module(s): Module 3.E: Special Topics in Finance & Economics – Electives II
• Language: EN
• Mandatory: No

Fund Channel SA (professor: Olivier MARCY): In full autonomy, be able to select the reliable information sources, to analyze the information accuracy. Get the right information at the right moment, it is key and critical in finance.

Alternative liquid investments (professor: Edoardo ANCORA): At the end of the course the students will have a complete overview of the valuation techniques applied in Luxembourg for private equity, real estate and private debt. The students will be able to become familiar with the practical aspects to perform and assess a valuation of an alternative and illiquid investment.

Let’s set up an asset management business (professor: Nicolas DELDIME): Understand the strategic concerns of entrepreneurs who move to Luxembourg to do asset management. Have a holistic understanding of a regulated organizational model.

BCEE (professors: Yves BODSON, Philippe HENNES, Yves DOMINICY)

Risk Management: The first part of the lecture is an introduction to risk management answering the questions what risk is and what is risk management. The different types of risk in a bank will be introduced, as well as the Banking Union and the three lines of defense. In the second part of the lecture, we will concentrate on the credit risk, and we will be talking about credit scoring models.

Asset Classes and securitization: Presentation of the primary financial asset classes (key legal and economical definitions, interaction between economic and financial cycles, valuation criteria applicable to fixed income instruments and to equities, risk categories and key measurement tools) and of the securitization market (key concepts with references to the primary financial asset classes and evolving legal and economic landscape)

Corporate Banking: The presentation will address the management of the bank’s commercial relationship with corporate/professional clients, with the main theme of business financing.

Fund Channel SA (professor: Olivier MARCY)

Information Hierarchy (Duality between, media objectives and public targeted, versus, financial resources)

Part I:

Brief history of the messages and the information channels and approach of the Media theories

News-papers – Information hierarchy

Part II:

Impact of the digitalization

Web platforms and social media – information hierarchy

Social Media information hierarchy – (student presentations – 2/4 per groups)

 

Alternative liquid investments (professor: Edoardo ANCORA)

In a time of low-interest, low inflation and high turbulence in Stock Exchanges, investment managers are engaged in the hunt of higher yields like never before, starting to explore the alternative and illiquid investment space.

In this context, a key skill required is the capability to properly value alternative and illiquid assets. During the course we will explore the main alternative investments (private equity, real estate, and private debt) and the typical valuation techniques applied by investment managers in Luxembourg.

 

Let’s set up an asset management business (professor: Nicolas DELDIME)

1. Asset management is a regulated profession. What does it mean to practice a regulated profession?

2. Build an organizational model (governance, staffing, insourcing / outsourcing, different stakeholders)

3. Build a business plan

4. Review of a practical case

BCEE (professors: Yves BODSON, Philippe HENNES, Yves DOMINICY): 3 hours written exam

Fund Channel SA (professor: Olivier MARCY): 20mn presentation during course period

Alternative liquid investments (professor: Edoardo ANCORA) :1 hour written exam

Let’s set up an asset management business (professor: Nicolas DELDIME): 1 hour written exam

The final grade is the aggregation of the 4 exams’ grade (weighted / Teaching Units)

Literature

Information Hierarchy – Fund Channel SA (professor: Olivier MARCY):
Digital Platforms :
Fundchannel.com
Bloomberg.com
Morningstar.com
Fundinfo.com
Fundsquare.net
SwissFundData.ch
six-group.com
Books :
Media et Société (Francis Balle – Montchrestien – 7ème Edition – 2019)
Culture Numérique (Dominque Cardon – Les presses de Sciences Po, Coll. « Les petites humanités » 2019)
Histoire des Médias (Jacques Attali Fayard – 2021)
Deux Mille Mots pour dire le Monde ( Henriette Walter – Bouquins – 2021)
Histoire politique de la roue (Raphaël Meltz – Librairie Vuibert – 2020)
Histoire de la Rue de l’Antiquité à nos Jours ( Danielle Tartakowsky – Tallandier 2022)
Les Algorithmes font la loi (Aurélie Jean – Livre de poche – 2023)
Histoire Mondiale des Impôts de l’Antiquité à nos jours (Eric Anceau, Jean-Luc Bordon Passés / Composés – 2023)
No Crypto ( Nastasia Hadjadji – 2023)
Technopolitique (Asma Mhalla – Seuil – 2024)
Newspapers MultiMedia Platforms :
Financial Times (UK World Wide)
The New Yorker
BBC
The Economist
South China Morning Post
Jeune Afrique
Les Echos (FR)
L’Echo De Tijd (BE)
Handelsblatt  (DE)
Beaux Arts Magazine (FR)
Le courrier International

Let’s set up an asset management business (professor: Nicolas DELDIME):Circular Commission de Surveillance du Secteur Financier 18/698

• Course title: 4.E1.Design Execution and Evaluation of Research in Finance and Economics
• Number of ECTS: 5
• Course code: MScFE_BK-12
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: EN
• Mandatory: Yes

On completion of the course unit successful students will be able to:

• 
  Execute an academic research project: identify a research question, position it in the context of current literature, formulate hypotheses and apply appropriate methodology to test them, present results and discuss the implications of the analyses that have been carried out; critically assess academic research and the work of peers.
  

• 
  The course is intended to equip students with the necessary knowledge and tools to carry out independent research and prepare them for the master thesis – either academic or the research section of the applied thesis.
  

The course aims at developing and strengthening students’ academic skills in identifying and analyzing research questions, applying research methods in finance and economics to approach these questions, and interpreting results relative to the current state of the art.The course is centered around a set of research projects in Finance and Economics that students carry out in small research teams. Each team focuses on an individual research project and carries it out along all its stages, from identifying a research question and motivating its relevance to choosing appropriate methodology to address it, executing the analysis, and presenting and interpreting the results.The course is structured as follows:

1. Introductory session on a selected set of research topics, providing guidance on identifying each respective research question, on the appropriate research methodology and empirical strategy, the respective data to use for the empirical analysis, and related literature.

2. Execution stage during which research teams plan and conduct their chosen research projects.

3. Student presentation sessions at each stage of the execution of the research projects:

3.1.     Research idea/question, motivation behind it and current state of the art.3.2.     Research methodology, empirical setup, development of hypotheses, overview of the data needed to address the research question.Execution and analysis of results, potential further areas of inquiry, conclusion.

60% Seminar paper

30% Presentation

10% Discussion

Academic articles that represent the key references for the set of research projects distributed ahead of the course.

• Course title: 4.E2 Financial Econometrics
• Number of ECTS: 5
• Course code: MScFE_BK-22
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to: 
Show proficiency in probability and statistics, calculus, programming and use these tools to model markets and drive decision making
Understand risk and analyze financial data
Design and implement complex financial models that allow financial firms to price and trade securities
Understand the current academic and practitioner literature on financial engineering
Get exposed to some of the most applicable machine learning techniques in finance considering that machine learning and Artificial Intelligence (AI) play a significant role in the creation of models

Understanding risk and analysing data to drive policy and decision making is the name of the game in institutions like i.e. banks, insurance companies, hedge funds, and governments.Financial Engineering is the study of applying math, statistics, computer science, economic theory, and other quantitative methods to analysing and modelling financial markets. Financial engineers work at the intersection between data science and finance. The first financial engineers were Fischer Black, Robert Merton, and Myron Scholes, infamous for their options pricing model known as the Black-Scholes Model. This model won the Nobel prize in economics and is the foundation for the explosion in derivative markets. 

2h written exam

Literature : 

“Statistics and Data Analysis for Financial Engineering”, 2nd edition by David Ruppert and David 
S. Matteson, Springer, ISBN 978-1-4939-2614-5 (eBook)
Research papers

• Course title: 4.E3 Statistics in risk management using R – French
• Number of ECTS: 5
• Course code: MScFE_BK-16
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: FR
• Mandatory: No

Objectifs:

Savoir manipuler le logiciel R pour des analyses de données

Comprendre la notion de risque financier et son estimation en utilisant les fonctionnalités du logiciel R

Savoir faire une analyse économétrique de séries temporelles en utilisant le logiciel R

Le premier chapitre du cours consiste à apprendre à manipuler le logiciel R, notamment afin d’avoir accès à des données financières et de les analyser. Nous étudierons en particulier comment créer des séries temporelles, utiliser des indicateurs statistiques, faire une analyse technique et graphique à l’aide du logiciel R.  Le deuxième chapitre s’intéresse au concept de risque financier d’un actif et d’un portefeuille d’actifs au travers de la notion de rendement, de volatilité, de calcul d’indicateurs de performance ajustés au risque et d’optimisation de portefeuille. Nous développerons la notion de risque au travers d’indicateurs statistiques tenant compte des risques extrêmes. Dans tous ces chapitres, les séries temporelles sont étudiées en faisant une analyse économétrique des données financières. Les applications qui illustrent le cours sont entièrement menées à partir du logiciel R. Les étudiants doivent avoir téléchargé le logiciel R avant de venir à la première séance du cours en utilisant le lien suivant : https://cran.r-project.org/bin/windows/base/

Examen écrit de 2 heures pendant la période de cours

Bilbiographie :

Analyse des séries temporelles, 5e édition, de R. Bourbonnais et V. Terraza, chez Dunod 2022
Analyse Statistique pour la gestion bancaire et financière, applications avec R, de V. Terraza et C. Toque, chez De Boeck, 2013
Modélisations de la Value at Risk – Une évaluation de l’approche Riskmetrics de V. Terraza Editions Universitaires Européennes – 2010 
Le livre de R, apprentissage et référence, de B. Desgraupes, chez Vuibert, 2013
Séries temporelles avec R, de Y. Aragon, chez Springer, 2011

• Course title: 4.E4. Incubator Course: Cases of Modern Finance
• Number of ECTS: 5
• Course code: MScFE_BK-26
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: EN
• Mandatory: No

On completion of the module unit successful students will be able to:

• Have a broader understanding about how emergent financial technologies are changing the standard Financial Architecture and its implications for financial institutions


• Have a better understanding of how emergent FinTech developments will change the financial products available

This incubator course aims to

• Educate students about the disruptive effects of advanced technologies in the financial sector, with a leading focus on asset management, instruments and advanced data analytics.
• Raise awareness on both the socio-economic and the performance implications of innovation diffusion.
• Provide both academic and industry-strength underpinning for the future of finance.
  

The incubator course consists of two parts. Each part includes both lectures lectures and practical case work in groups. The practical case works, as well as the lectures are offered by a residing institutional / industry partner of this course. Students are asked to provide solutions to the presented case studies, based on a problem-based discovery.
The first block is concentrating on the notion “Financial architecture” in the context of asset management and administration. It discusses the disruptive effects of peer2peer technologies, such as blockchains, through industry-strength case studies. Below, the skeleton of this block is provided as follows:The second block is concentrating on the notion “advanced data analytics” in the context of asset management and administration. In an ever-changing environment, Investment Banks face many challenges: regulatory compliance, business model review and increased competition. At the same time, the financial sector is witnessing a huge digital transformation, particularly with the advent of new technologies. Artificial intelligence, Distributed Ledger Technology, API and Big data, how can new tools bring solutions to financial institutions?This block would kick-off with a lecture on investment banking. Through its organizational structure and its businesses, themes common to the world of banking and investment will be discussed. Case studies are introduced and discussed in order to propose a digital solution in response to the case problem at hand. Below, the skeleton of this block is provided as follows:Conclusion

• General Introduction
• Introduction to the Financial Architecture
• Business Models of Banks
• Business Models of the Asset Management Industry
• Trading Platforms and their Market Microstructure
• Introduction to Blockchain Peer-to-peer platforms
• The Blockchain Market Microstructure 
• Business Models of FinTech Credit Platforms
• Recent developments about Financial Products on Peer-to-peer platforms
• Workshop: Liquidity pools and automated market makers (instrumentalising flash swaps and loans)
• Conclusion: A perspective on the changing Financial Architecture in light of recent technological developments.

• A practical application of Performance and Risk management measures in finance
• Measures of return
• The complexity of defining and measuring risk
• How to find the best returns while minimizing the risk (measures of risk-adjusted return and its limitations)?
• Can we use new technologies and Machine Learning for predicting stock price? How to observe the factors affecting assets?
• Multifactor Models (Fama French, Carhart four-factor model): a linear approach
• An Introduction to Decision trees (and random forest): a nonlinear approach
• Workshop #1: Implementation of a basic strategy with alternative data: does ESG (Environmental, Social, and Governance) investment bring better performance?
• Workshop #2: Compare linear and non-linear approaches in price prediction and understand the limitations

The final grade will be an oral presentation (100%)

Literature:

Ayadi, R., Cucinelli, D. and De Groen P. (2019) « Banking Business Models Monitor : Europe »
Halaburda, H., Haeringer, G., Gans, J. and Gandal, N. (2022) “The Microeconomics of Cryptocurrencies”, Journal of Economic Perspectives forthcoming.   
Lehar, A. and Parlour, C. (2021) “Decentralized Exchanges”, SSRN Working Paper.
Liberti, J.M. and Petersen, M.A. (2019) “Information: Hard and Soft”, The Review of Corporate Finance Studies, 8(1), 1-41.
Valleee, B. and Zheng, Y. (2019) “Marketplace lending: A new banking paradigm?”, The Review of Financial Studies, 32(5), 1939-1982.
Verlaine, M. (2020) “Behavioral Finance and the Architecture of the Asset Management Industry”.
Stefan Jansen. (2020). Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition.
Chloé-Agathe Azencott. (2018). Introduction au Machine Learning.
Joachim Häcker, Dietmar Ernst. (2017). Financial Modeling: An Introductory Guide to Excel and VBA Applications in Finance.
Jean Dermine. (2009). Bank Valuation and Value-Based Management: Deposit and Loan Pricing, Performance Evaluation, and Risk Management.
John C. Hull. (2018). Risk Management and Financial Institutions.

• Course title: 4.E5 Topics in Well-being Research
• Number of ECTS: 5
• Course code: MScFE_BK-28
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to:

• 
  Analyse different datasets with the STATA software
  


• 
  Have knowledge of a number of the empirical methods used to tackle theoretical questions
  


• 
  Have acquired advanced interdisciplinary knowledge.

 

In this course, students will learn about the different theoretical and conceptual approaches used in the social sciences to analyze well-being and its determinants. After an introduction to the various methods proposed for the measurement of well-being, we will focus on its determinants and explore the relationship with income (including the Easterlin paradox), social position and mobility, as well as the role of adaptation and expectations. We will then move on to discuss societal well-being and the recent proposals to go beyond GDP as a measure of progress. Last, we will show how measures of individual well-being can be used to shed new light on important economic concepts such as income inequality, gender disparities and policy evaluation.

Seminar Paper (95%)

Active participation (5%)

Recommended literature:
Balestra, C., Boarini, R. Ruiz, N. (2018).
Going beyond GDP: empirical findings, in: C. D’Ambrosio (ed.), Handbook of Research on Economic and Social Well-Being, chapter 2, pages 52-103, Edward Elgar Publishing.
Blanchflower, D. G., Oswald, A. J. (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88, 1359-1386.
Blanchflower, D. G., Oswald, A. J. (2008). Is well-being U-shaped over the life cycle? Social Science Medicine, 66, 1733-1749.
Card, D., Mas, A., Moretti, E., Saez, E. (2012). Inequality at work: The effect of peer salaries on job satisfaction. American Economic Review, 102, 2981-3003.
Clark, A. E., Oswald, A. J. (1996). Satisfaction and comparison income. Journal of Public Economics, 61(3), 359-381.
Clark, A. E., Frijters, P., Shields, M. A. (2008). Relative income, happiness, and utility: An explanation for the Easterlin paradox and other puzzles. Journal of Economic Literature, 46, 95-144.
Clark, A. E., Lepinteur, A. (2019). The causes and consequences of early-adult unemployment: Evidence from cohort data. Journal of Economic Behavior Organization, 166, 107-124.
Clark, A. E. (2018). Four decades of the economics of happiness: Where next? Review of Income and Wealth, 64, 245-269.
Di Tella, R., MacCulloch, R. J., Oswald, A. J. (2001). Preferences over inflation and unemployment: Evidence from surveys of happiness. American Economic Review, 91, 335-341.
Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In Nations and Households in Economic Growth. Academic Press.
Flèche, S. (2021). The welfare consequences of centralization: evidence from a quasi-natural experiment in Switzerland. Review of Economics and Statistics, 103, 621-635.
Giovannini, E., Rondinella, T. (2018). Going beyond GDP: theoretical approaches, in: C. D’Ambrosio (ed.), Handbook of Research on Economic and Social Well-Being, chapter 1, pages 1-51, Edward Elgar Publishing.
Guio, A.-C. (2018). Multidimensional poverty and material deprivation: empirical findings, in: C. D’Ambrosio (ed.), Handbook of Research on Economic and Social Well-Being, chapter 6, pages 171-193, Edward Elgar Publishing.
Layard, R. (2006). Happiness and public policy: A challenge to the profession. Economic Journal, 116, C24-C33.
Lepinteur, A. (2019). The shorter workweek and worker wellbeing: Evidence from Portugal and France. Labour Economics, 58, 204-220.
Luttmer, E. F. (2005). Neighbors as negatives: Relative earnings and well-being. Quarterly Journal of Economics, 120, 963-1002.
Oswald, A. J., Proto, E., Sgroi, D. (2015). Happiness and productivity. Journal of Labor Economics, 33, 789-822.

• Course title: 4.E6 Data Protection for Official Statistics
• Number of ECTS: 1
• Course code: MScFE_FE-12
• Module(s): Module 4.E Special Topics in Finance and Economics – Electives III
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to: -Distinguish between the various legal regimes applicable to data in the EU;-Identify the specific legal obligations relevant to their work;-Understand the core elements of the legal regime governing personal data in the EU;-Engage productively with legal professionals towards compliance with data-related obligations;-Apply best practices on data protection to their technical work.

Data is an essential input for statistical work. Increasingly, the processing of that data is subject to various kinds of legal requirements. Some of those requirements, such as those relating to information that is associated or can be associated with specific individuals (personal data), are meant to safeguard individuals and groups from potential risks related to the processing of their data. Others, such as data portability and interoperability requirements, are meant to maximize the positive externalities from data collection and usage. Amid this network of legal entanglements, professionals need to be able to identify their legal duties and the best ways to complying with them during their work activities.
This course aims to provide an overview of key legal concepts that are relevant for professionals who engage in activities involving the processing of personal data, with particular emphasis on the specific challenges that emerge in the context of official statistics. In particular, the course addresses the following concepts:
-The landscape of EU data law: personal data protection and other data-related pieces of legislation
-The scope of personal data protection: when are data protection obligations applicable?
-General rules and principles applicable to data processing
-Rights of the data subject
-Data protection by design and by default
-Cybersecurity as a data protection concern
-Special regime for research data
-Responsibility for data misuse

Short readings will be shared with the students before the course. Additionally, the course will draw from the following background sources:
-Marco Almada, Law Compliance in AI Security Data Protection (European Data Protection Board 2025).
-Article 29 Working Party, ‘Guidelines on transparency under Regulation 2016/679’ (2018).
-Laurence Diver and Pauline McBride, ‘Argument by Numbers: The Normative Impact of Statistical Legal Tech’ (2022) 3 Communitas: théories et pratiques de la normativité.
-European Data Protection Board, ‘Guidelines 4/2019 on Article 25 on Data Protection by Design and by Default’ (2020).
-European Union Agency for Fundamental Rights, Handbook on European data protection law (Publications Office of the European Union 2018).
-Mireille Hildebrandt, ‘Law as Computation in the Era of Artificial Legal Intelligence: Speaking Law to the Power of Statistics’ (2018) 68 University of Toronto Law Journal 12.
-Christopher Kuner, Lee A Bygrave and Docksey, Christopher (eds), The EU General Data Protection Regulation (GDPR): A Commentary (Oxford University Press 2020).
-Paul Ohm, ‘Focusing On Fine-Tuning: Understanding The Four Pathways For Shaping Generative AI’ (2024) 25 Columbia Science and Technology Law Review 214.
-Indra Spiecker gen. Döhmann and others (eds), General Data Protection Regulation: Article-by-article commentary (Beck; Nomos; Hart Publishing 2023).

• Course title: 4.Applied Master Thesis (including Internship)
• Number of ECTS: 20
• Course code: MScFE_BK-19
• Module(s): Module 4.PAT.Master Thesis
• Language: EN
• Mandatory: No

• Course title: 4.Academic Master Thesis
• Number of ECTS: 20
• Course code: MScFE_BK-20
• Module(s): Module 4.PAT.Master Thesis
• Language: EN
• Mandatory: No