• 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 explanationsKnow the foundations of behavioural finance and behavioural economicsUnderstand 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, 1053-1128Dhami, Sanjit, 2016, Foundations of behavioral economic analysis. Oxford University PressGigerenzer and Selten (Eds.) 2002. Bounded rationality: The adaptive toolbox. MIT pressHens, Thorsten and Kremena Bachmann, 2009, Behavioral Finance for Private Banking, WileyKahneman and Tversky 1979 “Prospect Theory: An Analysis of Decision under Risk” EconometricaShleifer Andrei, 2001, Inefficient markets – An introduction to behavioral finance. Calderon Lectures in EconomicsThaler 1985 “Mental accounting and consumer choice”. Marketing science, 4(3), 199-214Thaler 1999 “Mental accounting matters”. Journal of Behavioral decision making, 12(3), 183-206Thaler and Johnson 1990 “Gambling with the house money and trying to break even”. Management science 36, 643-660Tversky and Kahneman 1992 “Advances in prospect theory”. Journal of Risk and uncertainty, 5(4), 297-323Further 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 makingUnderstand risk and analyze financial dataDesign and implement complex financial models that allow financial firms to price and trade securitiesUnderstand the current academic and practitioner literature on financial engineeringGet 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 theoriesNews-papers – Information hierarchyPart II:Impact of the digitalizationWeb platforms and social media – information hierarchySocial 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 plan4. Review of a practical case

BCEE (professors: Yves BODSON, Philippe HENNES, Yves DOMINICY): 3 hours written examFund Channel SA (professor: Olivier MARCY): 20mn presentation during course periodAlternative liquid investments (professor: Edoardo ANCORA) :1 hour written examLet’s set up an asset management business (professor: Nicolas DELDIME): 1 hour written examThe final grade is the aggregation of the 4 exams’ grade (weighted / Teaching Units)

LiteratureInformation Hierarchy – Fund Channel SA (professor: Olivier MARCY):Digital Platforms :Fundchannel.comBloomberg.comMorningstar.comFundinfo.comFundsquare.netSwissFundData.chsix-group.comBooks :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 YorkerBBCThe EconomistSouth China Morning PostJeune AfriqueLes Echos (FR)L’Echo & De Tijd (BE)Handelsblatt  (DE)Beaux Arts Magazine (FR)Le courrier InternationalLet’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 dataApply state-of-the-art risk measurement and risk management techniquesAssess the performance of the econometric models in describing the time varying VaRCritically appraise risk management systems

 

Part 1 – BackgroundRisk Management and Financial ReturnsHistorical Simulations, Value-at-Risk and Expected ShortfallA Primer on Financial Time Series Analysis Part 2 – Univariate Risk ModelsVolatility Modeling Using Daily DataVolatility Modeling Using Intraday DataNonnormal Distributions Part 3 – Multivariate Risk ModelsCovariance and Correlation ModelsSimulating the Term Structure of RiskDistributions 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, 2012Risk Management and Financial Institutions, Wiley, ed. 3, John Hull, 2012 (optional)Financial Risk Manager Handbook, Wiley, ed. 5, Philippe Jorion, 2009 (optional)

• Course title: 3.FET1.Econometrics (III) (STATA)
• Number of ECTS: 5
• Course code: MScFE_FE-1
• Module(s): Module 3.FET: Specialisation Financial Economics Track
• Language: EN
• Mandatory: Yes

• Understand the fundamentals of causal inference and its importance in empirical research• Familiarise with recent advancements in causal inference methods, especially those aimed at handling heterogeneity in treatment effects• Understand the underlying assumptions and identification strategies of most common causal inference methods• Develop the ability to critically evaluate empirical studies using causal inference methods• Apply the learned techniques to real-world datasets and research questions, and interpret the results accurately• Develop problem-solving skills for tackling complex econometric challenges and making informed decisions about appropriate methods

The aim of this course is to equip students with advanced skills and in-depth knowledge in causal inference with a specific emphasis on handling heterogeneous treatment effects. The first part of the course provides a general review on causal inference based on the potential outcomes framework, the link between regression and causality, the estimation of treatment effects using methods that rely on the conditional independence assumption (IPW, AIPW, IPWRA, Matching) and instrumental variables with heterogeneous treatment effects (LATE). The second part of the course focuses on the methods of difference-in-differences and regression discontinuity designs covering new developments. The course aims to provide a solid understanding of the underlying assumptions and identification strategies of these methods and provide hands-on training with STATA enabling students to apply these methods effectively to real-world datasets and empirical research questions.

Assessment Modality

Combined or continuous assessment

Task 1

Mid-term exam

 

0-20

30 %

Specific Assessment Rules

1h

 

Task 2

Written exam

 

0-20

70%

Specific Assessment Rules

2h

 

Literature:

Slides and do-files are available on Moodle.

 

• Course title: 3.FET2.Programming R and applications
• Number of ECTS: 5
• Course code: MScFE_FE-2
• Module(s): Module 3.FET: Specialisation Financial Economics Track
• Language: EN
• Mandatory: Yes

• Have the knowledge of the principal features of the R software: manipulate data, graphs, key statistical functions• Programm with R• Manipulate time series analysis with R • Use financial applications

This course: • provides first an overview and applications of R software so that students can learn how to use it for statistical analysis. This will involve manipulating data, creating graphs, and utilising statistical functions. • focuses then on using R to process time series data. By manipulating and exploring some examples, students will be able to understand the financial series structure for modeling and predicting returns and volatility.

Assessment ModalityCombined or continuous assessmentAssessment TasksType of AssessmentGrading SchemeWeight for final GradeTask 1Take-home exam + active participation 0-2050%Specific Assessment RulesOral presentation Task 2Written exam 0-2050%Specific Assessment Rulessubmission of a paper  

Literature:• Le livre de R, apprentissage et référence, de Bernard Desgraupes, chez Vuibert, 2013• Statistiques avec R de Pierre- André Cornillon et al. Presses Universitaires de Rennes, 3ième édition, 2012• Séries –temporelles avec R, de Yves Aragon, chez Springer, 2011• Analyse Statistique pour la gestion bancaire et financière, applications avec R, de Virginie Terraza et Carole Toque, chez De Boeck, 2013• Modélisations de la Value at Risk- Une évaluation de l’approche Riskmetrics- de Virginie Terraza Editions Universitaires Européennes- 2010• Analyse des séries temporelles, de Régis Bourbonnais et Virginie Terraza, Dunod 2022• Time series analysis and its applications with R examples, Shumwey, Stoffer, 4 ed, Springer 2017 • Time Series, a data analysis approach using R, Shumwey, Stoffer, Taylor and Francis Group, 2019• Time series analysis and forecasting using Python and R, Jeffer Strickland, Lulu, Inc 2020

• Course title: 3.FET3.Microeconomics of Banking
• Number of ECTS: 5
• Course code: MScFE-22
• Module(s): Module 3.FET: Specialisation Financial Economics Track
• Language: EN
• Mandatory: Yes

• Understand the risk structure of bank balance sheets using annuity analysis• Understand how to price stocks and bonds using the Lucas-tree model, and the linkups between macroeconomic performance, stock prices, and bond interest rates• Determine bank fragility through the Diamond-Dybvig model• Use the Kiyotaki-Moore model in order to understand how credit cycles can create an environment that encourages excess risk-taking by banks• Understand some determinants of credit rationing• Model how interbank loan markets can help banks in order to pool risks in order to provide robust deposit contracts

This course intends to provide a clear analysis of questions such as, how banks complement existing capital markets, how banks can potentially increase social welfare, why asset-price fluctuations can be severe and cause problems to banks, why there can be credit cycles and credit-rationing challenges, and how poor credit-rationing channels can be mitigated by interbank markets and Central-Bank regulation. The course pays attention to providing an understanding of market forces behind bank competition, asset-market fluctuations, credit cycles and bank fragility. By understanding deep microeconomic determinants and fundamentals of market forces in capital markets and the interplay between creditors and borrowers along cycles, students should be able to gain insights on the changing bank regulatory environment and on how to adapt to new borrowing/lending technological innovations.

 

Assessment Modality

Combined or continuous assessment

Assessment Tasks

Type of Assessment

Grading Scheme

Weight for final Grade

Task 1

Written exam

 

0-20

70 %

Specific Assessment Rules

2h

 

Task 2

Other, please specify

 

0-20

30 %

Specific Assessment Rules

Problem sets and homework assessment

 

Literature:

• Microeconomics of Banking” by Xavier Freixas and Jean-Charles Rochet (MIT Press 1st or 2nd edition)• Diamond DW, Dybvig PH (1983). “Bank runs, deposit insurance, and liquidity”. Journal of Political Economy. 91 (3): 401–419. CiteSeerX 10.1.1.434.6020. doi:10.1086/261155. JSTOR 1837095• Kiyotaki, Nobuhiro & Moore, John (1997). “Credit Cycles”. Journal of Political Economy. 105 (2): 211–248. doi:10.1086/262072• Stiglitz, Joseph E.; Weiss, Andrew (June 1981). “Credit rationing in markets with imperfect information”. The American Economic Review. American Economic Association via JSTOR. 71 (3): 393–410.• Williamson, S.D, 1987. “Costly Monitoring, Loan Contracts, and Equilibrium Credit Rationing” The Quarterly Journal of Economics, vol. 102, pages 135-146.• Bhattacharya, Sudipto and Douglas Gale (1987). “Preference Shocks, Liquidity and Central Bank Policy”, in W. Barnett and K. Singleton (eds.), New Approaches to Monetary Economics, Cambridge University Press, 69-88.

 

 

• Course title: 3.FET4.Survey Data in the Fields of Economics and Finance
• Number of ECTS: 5
• Course code: MScFE_FE-4
• Module(s): Module 3.FET: Specialisation Financial Economics Track
• Language: EN
• Mandatory: Yes

–  –  • Understand the importance of survey data in Economics and Finance in addition to usual macro-economic aggregates• Learn the main concepts and definitions used in survey design and analysis• Analyse survey data taking their main features into account to produce relevant statistical indicators in the fields of Economics and Finance

• Main concepts and terminology in survey sampling (target population, sample, parameter, estimator, bias, variance…)• Presentation of the most commonly used sampling designs (simple random sampling, stratified sampling, unequal probability sampling and multi-stage sampling)• Presentation of concrete survey examples in the fields of Economics and Finance (SILC, HFCS, SAFE etc.)• Introduction to survey data analysis (main indicators, sample weighting, variance estimation…)• Numerical examples in Stata based on real survey data (SILC, HFCS, etc.)

Assessment Modality

Combined or continuous assessment

Assessment Tasks

Type of Assessment

Grading Scheme

Weight for final Grade

Task

Written exam

 

0-20

100%

Specific Assessment Rules

One mid-term theoretical exam (90 minutes) and one final computer-based exam (90 minutes)

 

Literature:

• S. Lohr (2010), Sampling design and analysis. Cengage Learning• K.M. Wolter (2007), Introduction to Variance Estimation. Springer• W.G. Cochran (2007), Sampling techniques. Wiley• P. Ardilly and Y. Tillé (2006), Sampling methods: exercises and solutions. Springer• Y. Tillé (2006), Sampling and estimation from finite populations. Technip

• Course title: 3.FET5.Impact Evaluation
• Number of ECTS: 5
• Course code: MScFE_FE-5
• Module(s): Module 3.FET: Specialisation Financial Economics Track
• Language: EN
• Mandatory: Yes

The aim of this course is to learn how an applied economist can contribute to impact evaluations and find out the latest developments and achievements in this field. We will use applied micro-econometrics, but the main objective is to be able to design an impact evaluation from the beginning to the end. It is therefore not a technical course but we will use statistical and econometrical concepts. We will also look critically at the claims made by economists using impact evaluations and the functioning of the industry worldwide. Examples will mostly come from developing countries but are also relevant for developed ones.

 

The course is also hands-on in three respects. First, it offers an introduction on the gold standard way (experimental and quasi-experimental design) of conducting rigorous impact evaluations using high standard academic papers. Then, it confronts these models to the practice, and discusses how to remain rigorous while dealing with all the constraints from the field. Students will be asked to reflect on different designs in practice. Second, students will be invited to use STATA effectively for impact evaluation purposes with data from real-life cases. And third, the course will cover all aspects of an impact evaluation: from the requests emanating from donors and policy makers, who commission those evaluations, to the work of the evaluators/researchers on the field, including the point of view of the ones that are evaluated (the intervention partners and beneficiaries).

The evaluation of public policies, programs and interventions has grown tremendously in the last decade. This course will deal with all aspects of impact evaluations in theory as well as in practice. The focus of the course will be the design of the most appropriate evaluation approach to measure the impact of an intervention / public policy. Why and when is it useful to evaluate? How to build a solid evaluation matrix with appropriate indicators? How do we evaluate given the multiple constraints on the ground? How do we communicate on the results? How can an effective evaluation be scaled up?  Starting from the most rigorous approach, randomised evaluation designs, we continue with quasi-experimental approaches, and mixed method approaches, confronting each design with its limitations and constraints. 

Assessment Modality

Combined assessment

Assessment Tasks

Type of Assessment

Grading Scheme

Weight for final Grade

Task 1

Written exam

 

0-20

60%

Specific Assessment Rules

2h

 

Task 2

Other, please specify

2 assignments during the course

Choose an item.

40%

Specific Assessment Rules

Click or tap here to enter text.

 

Literature:

Running Randomized Evaluations, Glennester and Takavarasha, 2013 Princeton UP

Broadening the range of designs and methods for impact evaluations, DFID, 2012

Introduction to mixed methods in impact evaluation, M. Bamberger, 2012

Good Economics for Hard Times, Banerjee & Duflo (2019)

We will also distribute additional papers from the literature and other teaching material.

• 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 paper30% Presentation10% 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:Why do goalkeepers not always choose to jump to the same side at penalty kicks? Why did performance inequality increase so much in the last 5 decades in the peloton of the Tour de France?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? 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): 155-180DUGGAN, 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 donnéesComprendre la notion de risque financier et son estimation en utilisant les fonctionnalités du logiciel RSavoir 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 2022Analyse Statistique pour la gestion bancaire et financière, applications avec R, de V. Terraza et C. Toque, chez De Boeck, 2013Modélisations 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, 2013Séries 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 institutionsHave 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:General IntroductionIntroduction to the Financial ArchitectureBusiness Models of BanksBusiness Models of the Asset Management IndustryTrading Platforms and their Market MicrostructureIntroduction to Blockchain Peer-to-peer platformsThe Blockchain Market Microstructure Business Models of FinTech Credit PlatformsRecent developments about Financial Products on Peer-to-peer platformsWorkshop: 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.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:A practical application of Performance and Risk management measures in finance Measures of returnThe complexity of defining and measuring riskHow 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 approachAn Introduction to Decision trees (and random forest): a nonlinear approachWorkshop #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 limitationsConclusion

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 softwareHave knowledge of a number of the empirical methods used to tackle theoretical questionsHave 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.FET.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.FET.Master Thesis
• Language: EN
• Mandatory: No