• Course title: 3.BT1.Financial Intermediation
• Number of ECTS: 5
• Course code: MScFE_BK-1
• Module(s): Module 3.BT: Specialisation Banking Track
• Language: EN
• Mandatory: Yes

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

• Understand the rationale for the existence of financial intermediaries


• Understand the bank funding issues


• Understand the bank lending issues


• Assess the digital-bank revolution

1. Banking function__UL_BLOCK__0__
2. Bank funding__UL_BLOCK__1__
3. Bank lending__UL_BLOCK__2__
4. Digital-banking revolution

• Transactional and relationship loans
• Sources of credit information
• Banks and financial markets
• Shadow banking
• Credit scoring
• On-line platforms
• Direct lending

The spot-lending decision

50% written exam
35% take-home exam
15% quizes, problem sets and class participation

BIBLIOGRAPHY:

 

Greenbaum, Stuart I., Anjan V. Thakor, and Arnoud WA Boot. Contemporary financial intermediation. Academic Press, 2019 [GTB]

• Course title: 3.BT2.Banking Operations and Strategy
• Number of ECTS: 5
• Course code: MScFE_BK-2
• Module(s): Module 3.BT: Specialisation Banking Track
• Language: EN
• Mandatory: Yes

Learning outcomes – This course will help participants to:

• Develop a strategic mindset in the fast changing banking world


• Interpret a bank balance sheet and key financial metrics


• Understand ‘maturity transformation’ and the liquidity mismatch as a fundamental ingredient


• Grasp regulatory and prudential requirements that European banks must adhere to


• Comprehend the various risks faced by banks and how these are measured, mitigated and managed


• Acquire knowledge and insights related to the opportunities and challenges facing European banks


• Understand bank strategy levers and how to make a positive impact on the future of banking

Bank Foundation:

• A brief introduction to the Luxembourg financial center
• The ABC’s of banking
• Type of banks
• The principal-agent problem
• Accounting principle
• The incentive problem
• Liquidity and solvency
• Equity capital and cash reserves
• Procyclicality mechanism

Banking Regulation:

• Financial stability
• Prudential regulation & Basel framework
• Macro prudential perspectives
• EU Banking Union

Banking failures and crisis:

• The Great Depression and banking panics of 1930-1933
• The Savings and Loan debacle
• The Global Financial Crisis
• The European debt crisis
• The 2023 turmoil (incl. SVB case study)

Analysis of a bank’s balance sheet, income structure, liquidity management and ALM management

• Balance sheet: asset & liability analysis
• Income structure
• ALM basic concepts
• Managing the gaps, liquidity risk and ratio, some case studies
• Securitization

Banking strategy

• Value creation
• Value proposition & marketing
• Customer acquisition & customer segmentation
• Measurement & analysis of business model
• Pricing
• Distribution
• Innovation

Risk management

• Operational risk management
• Regulatory capital management
• Credit assessment & risk management
• Market risk & stress-testing
• Compliance & Audit

Banking for the future

• European contemporary issues
• The digital revolution
• The sustainability revolution

Personal perspectives

100% written exam

Literature will be provided at the beginning of the course.

• Course title: 3.BT3.Investment Funds
• Number of ECTS: 5
• Course code: MScFE_BK-3
• Module(s): Module 3.BT: Specialisation Banking Track
• Language: EN
• Mandatory: Yes

Upon completion of the course, successful students will

• Have a good understanding of investment funds, their regulatory environment, players in the fund industry, market operations
  

Specifically, the main topics covered in the course include:
Characteristics of investment funds
Players in fund markets (i.e., fund managers, distributors, depositaries/custodians, fund accountants, transfer agents, risk managers)
The key regulations for fund markets
Operational flows in fund markets

• 
  Risk managemenet for investment funds
  

 

This module describes the characteristics and operation of the investment fund industry, i.e., the legal framework of funds, the specific functions of depositaries and custodians, the operations of fund accounting and transfer agency, risk management for funds, the roles of the different players involved in the fund industry.
Investment Funds and their Legal Framework

• Types of funds, types of investors and different investment strategies
• Organisational and efficiency features
• Investor protection and disclosure requirements
• Regulation and supervision with a focus on the EU regulatory framework
• Asset management, UCITS management companies and alternative investment fund managers
  

Fund Depositaries/Custodians

Fund Accounting & Valuation

• Fund accounting: accounting rules, overview of main classical transactions (subscriptions, redemptions), portfolio transactions (shares, bonds, term deposit), derivative trades (options, futures),
• Corporate actions, e.g., dividends, mergers
• Fees, different types of fees (variable, fixed, sales charges, commissions), provisions and amortization
• NAV calculation and NAV controls: reconciliation process, portfolio variation, income, analysis of NAV variations (share classes), practical exercises

   
  

  NAV Risks

• Regulatory framework (e.g., Circular 02/77), investor protection against NAV errors
• Defining NAV errors
• Materiality and tolerance thresholds
• Financial impact and financial compensation
• Analysis of potential errors (materiality threshold)
• Exercises
  

Transfer Agency and Fund Dealing Services

Risk Management for Investment Funds

• Depositary/custody services, functions of depositaries, transaction settlement life cycle, managing settlement failures
• Origin & evolution of custody
• Asset servicing, custody operations, securities financing, CSDs, ICSDs
• Securities events, corporate actions
• Regulatory framework of custody, depositary/custodian fees
  

• Overview of fund dealing/transfer agency
• Players in shareholder services
• Types of accounts, opening accounts, KYC, AML/CFT
• Advice and distribution
• Fund dealing, settling fund orders, order processing systems
• Fund events, sales charges, fees, commissions, special shareholder services
• Regulatory environment of transfer agency
• Settlement
  

• Introduction to risk management for funds
• Market risk: (equities, interest rates, currencies, commodities)
• Credit risk (default risk, settlement risk, counterparty risk)
• Liquidity risk (asset liquidity risk, funding liquidity risk)
• Operational risk (processes, people, systems, external events)
• Managing risk in funds

50% written exam
50% other

BIBLIOGRAPHY:

UCITS Directive
CSSF Circulars
The Alternative Investment Fund Manager’s Directive (AIFMD)
Gremillion, Mutual Fund Industry Handbook, 2005
Habitant F-S, Handbook of Hedge Funds, 2009
Bogle JC, Bogle on Mutual Funds, 2015
CSSF regulations and circulars (e.g., 15/03, 16/644, 18/697, 11/512)
Markets in Financial Instruments Directive (MIFiD)
Commission Delegated Regulation 2013/231/EU (CDR)
Central Securities Depository Regulation 2014/909/EU (CSDR)
ESMA guidelines on CSDR
European Market Infrastructure Regulation 2012/648/EU(EMIR) 
ESMA guidelines on EMIR
The use of central bank money for settling securities transactions, European Central Bank, 2004
ALFI and ABBL guidelines and recommendations for depositaries, ALFI 2018

• Course title: 3.BT4.Insurance Techniques and Management
• Number of ECTS: 5
• Course code: MScFE_BK-4
• Module(s): Module 3.BT: Specialisation Banking Track
• Language: EN
• Mandatory: Yes

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

• The key role played by insurance at large in the society, in the financial industry, in particular in the Luxembourg financial industry


• The basic functioning of an insurance contract


• The various dimensions of the insurance regulatory framework, in particular the European Solvency II regulation


• How insurance techniques have been developed on the back of fundamental principles of risk theory


• The general principles of insurance premium and technical reserve, as well as how these two are calculated in the different insurance domains


• How insurance is accounted for through various valuation principles


• How to measure the performance of insurance companies


• The key challenges and opportunities ahead of the insurance industry in 2020

The course will be articulated around 5 chapters:

1. Introduction to insurance (9)__UL_BLOCK__0__
2. Risk theory and Insurance (12)__UL_BLOCK__1__
3. Insurance Techniques (15)__UL_BLOCK__2__
4. Value Management in Insurance (12)__UL_BLOCK__3__
5. Future opportunities for the insurance industry (2)

Vision from the markets

100% written exam

Teaching support (slides) will be shared with students.

• Course title: 3.BT5.Empirical Methods in Banking
• Number of ECTS: 5
• Course code: MScFE_BK-5
• Module(s): Module 3.BT: Specialisation Banking Track
• Language:
• Mandatory: Yes

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

• Familiarise with key empirical methods used by policy makers and academics to assess the effect of policies on the financial sector


• Independently apply the methods to real data


• Interpret and criticize the results of statistical analyses


• Critically assess the suitability of each method in different contexts


• Work in team and present the results of their own empirical study in front of an audience

Analysing bank dataMarket data versus accounting dataDescriptive statistics and data visualizationApplication: compare market-based systemic risk indicators to regulatory stress test outcomes
Introduction to panel data methodsFixed effects estimation and interpretationStandard errors clusteringApplication: the balance sheet channel of monetary policy transmission
Difference-in-differences analysisTreatment and control groupsIdentificationApplication: how did Dodd-Frank Act affect bank risk-taking incentives?
Event study methodologyDerive cumulative abnormal returns and standard errors forfirm specific events (earnings announcement, company elections)policy events (regulatory, monetary policy, political events)

• Application: assessing the effect of central bank interventions on bank and sovereign CDS spreads

60% written exam
40% presentation

BIBLIOGRAPHY:

• Angrist, J. D. and J.-S. Pischke (2009), Mostly harmless econometrics: an empiricist’s companion, Princeton University Press.


• Degryse, H., M. Kim and S. Ongena (2009), Microeconometrics of Banking: Methods, Applications, and Results, Oxford University Press.


• Freixas, X. and Rochet, J.C. (2008), Microeconomics of Banking, 2nd edition, MIT Press.


• W. Greene (2011), Econometric Analysis, 7th edition, Prentice Hall.

• Course title: 3.E1.Behavioural Finance
• Number of ECTS: 5
• Course code: MScFE_BK-6
• Module(s): Module 3.E: Special Topics in Finance and 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.E2.Household Finance and Real Estate
• Number of ECTS: 5
• Course code: MScFE_BK-7
• Module(s): Module 3.E: Special Topics in Finance and Economics – Electives II
• 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, do the critical reading of pertinent articles related to the question and know how to handle 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.


• 
  Orient themselves in the real world of household financial savings, debt and investments, up to current developments.
  

The objective of the course is to understand how households use financial instruments to attain their objectives. We first review the empirical facts on household wealth and inequality as well as the underlying rational and behavioral aspects of consumer financial decision making. We study current household financial products and the competitive landscape in credit, investment, and advising markets. We also cover consumer financial product innovations and the regulation of household finance, and provides an overview of recent research on real estate markets. The course includes a group project, where students will apply the concepts seen in class and make a presentation. We introduce various data sources on household finance, their advantages and disadvantages. We guide students in how to prepare their dataset for their own project in several dimensions (e.g. weighting and imputation) and in how to apply basic estimation techniques on household surveys with a complex sample design.

• Oral exam (40%)


• Presentation (40%)


• Seminar paper (20%)

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

• Course title: 3.E3.Financial Engineering
• Number of ECTS: 5
• Course code: MScFE_BK-8
• Module(s): Module 3.E: Special Topics in Finance and 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 and 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: 3.E5 Financial Econometrics
• Number of ECTS: 5
• Course code: MScFE_BK-22
• Module(s): Module 3.E: Special Topics in Finance and Economics – Electives II
• Language: EN
• Mandatory: No

Upon successful completion of this course students will be able to:

• Model the time varying conditional variance of time series data


• Apply state-of-the-art risk measurement and risk management techniques


• Assess the performance of the econometric models in describing the time varying VaR


• Critically appraise risk management systems

 

Part 1 – Background

• Risk Management and Financial Returns
• Historical Simulations, Value-at-Risk and Expected Shortfall
• A Primer on Financial Time Series Analysis

 

Part 2 – Univariate Risk Models

• Volatility Modeling Using Daily Data
• Volatility Modeling Using Intraday Data
• Nonnormal Distributions

 

Part 3 – Multivariate Risk Models

• Covariance and Correlation Models
• Simulating the Term Structure of Risk
• Distributions and Copulas for Integrated Risk Management

 

Part 4 – Backtesting and Stress Testing

100% written exam

The reference books for this course are:

• Elements of Financial Risk Management, Elsevier, ed. 2, Peter F. Christoffersen, 2012


• Risk Management and Financial Institutions, Wiley, ed. 3, John Hull, 2012 (optional)


• Financial Risk Manager Handbook, Wiley, ed. 5, Philippe Jorion, 2009 (optional)

• 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: No

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 Testing of Economic Model – Sports Data
• Number of ECTS: 5
• Course code: MScFE_BK-15
• 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:

• Formulate research questions.


• Use regulation/rules as a source of exogenous variation to estimate (economic) models.


• Test economic theory using sports data.


• Analyze sports data using economics principles.

Nowadays, a wealth of precise data is being collected about performances in sports. While statistical methods are designed to “summarize” the information contained in performance data, these methods do not properly take into account the fact that performances in sports are the result of rational agents (athletes, teams, coaches,…etc.) taking decisions under a set of incentives (rewards, prize money, payoffs) and constraints (rules of the game).
This course introduces and applies the economic approach to human behavior on performance data. The course shows how this approach can provide not only deep insights into the analysis of performance in sports but also robust tests for economic models. Indeed, since by the very nature of sports, athletes perform in a “controlled” environment, (changes in) the “rules of the game” offer unique opportunities, natural experiments, to test predictions of economic models.
The course provides a set of examples that are used as an illustration of the method. These examples relate to the following questions:

1. Why do goalkeepers not always choose to jump to the same side at penalty kicks? 
2. Why did performance inequality increase so much in the last 5 decades in the peloton of the Tour de France?
3. Why did the gender performance gap in triple jump, pole vault and marathon followed a S-shape evolution over a period of 25 years plateauing thereafter? 
4. Why do some tennis players and sumo wrestlers cheat?
   

2 hours written exam during the course period

PALACIOS-HUERTE, (2014), BEAUTIFUL GAME THEORY: HOW SOCCER CAN HELP ECONOMICS, PRINCETON UNIVERSITY PRESS.
CANDELON, B. AND DUPUY, A. (2015): HIERARCHICAL ORGANIZATION AND PERFORMANCE INEQUALITY: EVIDENCE FROM PROFESSIONAL CYCLING, THEINTERNATIONAL ECONOMIC REVIEW, VOL. 56 (4), PP. 1207-1236.
DUPUY, A. (2012): AN ECONOMIC MODEL OF THE EVOLUTION OF THE GENDER PERFORMANCE RATIO IN INDIVIDUAL SPORTS. THE INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT, VOL. 12 (1), PP. 224-245.
JETTER, M. AND WALKER, J. (2017): GOOD GIRL, BAD BOY: CORRUPT BEHAVIOR IN PROFESSIONAL TENNIS, SOUTHERN ECONOMIC JOURNAL, 2017, 84(1): 155-180
DUGGAN, MARK, AND STEVEN D. LEVITT. (2002). “WINNING ISN’T EVERYTHING: CORRUPTION IN SUMO WRESTLING .” AMERICAN ECONOMIC REVIEW, 92 (5): 1594-1605.

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