Programme

The track is divided into four modules. An investment management module covers fundamental subjects in portfolio management, alternative investments and quantitative models for investment decisions. Two elective modules cover a broad spectrum of special topics in finance. Finally, students are given the option to complete an internship with an applied master thesis, or to write an academic thesis.
Attendance at second year courses is compulsory.
Academic contents
The courses of the first year are part of the main common programme and not track specific.
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Master in Finance and Economics
Course offer for Investment Management, Semestre 3 (2024-2025 Winter)
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Details
- 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
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Objectives
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
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Description
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. -
Assessment
Written exam (2 hours) -
Note
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.
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Details
- 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
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Objectives
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.
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Description
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. -
Assessment
Oral exam (40%)Presentation (40%)Seminar paper (20%) -
Note
A comprehensive reading list of recent academic publications and working papers is available from the instructors.
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Details
- 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
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Objectives
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
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Description
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. -
Assessment
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%). -
Note
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
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Details
- 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
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Objectives
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.
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Description
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 -
Assessment
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) -
Note
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
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Details
- 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
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Objectives
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
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Course learning outcomes
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Description
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 -
Assessment
100% written exam -
Note
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)
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Details
- Course title: 3.IMT1.Fund Management and the Asset Management Industry
- Number of ECTS: 5
- Course code: MScFE_IM-1
- Module(s): Module 3.IMT: Specialisation Investment Management Track
- Language: EN
- Mandatory: Yes
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Course learning outcomes
• Discover the incredible diversity of skills required in the fund industry • Have the means to quickly discern the unexpected areas in which they can deploy their excellence• Understand the activity and the business of their future potential employers allowing them easier integration and multiplying their attractiveness potential for the successful jobs of tomorrow, through the learning of the various fund business models. -
Description
The Luxembourg financial sector has fantastically developed over the last 30 years around the fund industry. The objective of the course is to deliver to the student a comprehensive view of the key pillars of the fund industry which without them would not have made it possible to raise Luxembourg’s place to the rank of first European financial center in terms of investment funds. The Fund Management operational processes, the fund business and organizational modelsThe course will review the typical fund operational processes. The fund financial sector involves a surprising number of different professionals with distinct roles and value contribution. The most popular organizational models of fund managers will be reviewed and compared.Substance and governance requirements applicable to Luxembourg investment fund managersThe various financial crisis over the last 20 years have emphasized the need to ensure that fund managers behave with an appropriate degree of professionalism and in the interest of the people who entrust them with their money and savings for retirement. Successive waves of strengthening legislation around ethics, governance, internal control and supervisory framework by National and European Authorities have made the entry ticket for fund business entrepreneurs more expensive. The course will detail these requirements which have to be complied with or fund managers risks facing sanctions or to be eliminated from the market.Fund distributionThe course will also zoom into what made Luxembourg a fund marketplace known in the entire world: the fund distribution and the sales process.TaxationAt the end of the day, most investors hope that the money invested will generate returns and profits in proportion to the risk they have taken. But what happens if these profits are confiscated because the tax rules, at the level of the portfolio of investments, the fund itself or the personal taxation regime of investors have not been properly taken into consideration at an early stage in the fund set-up and distribution process? The course will introduce students to the various relevant tax concepts and tactics which, without mastery lead to potential business failure. -
Assessment
Assessment Modality
End-of-course assessment
Assessment Tasks
Type of Assessment
Grading Scheme
Weight for final Grade
Task
Written exam
0-20
100%
Specific Assessment Rules
2 h
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Note
Literature:
A set of self-contained slides will be distributed before the start of the course.
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Details
- Course title: 3.IMT2.Portfolio Management
- Number of ECTS: 5
- Course code: MScFE_IM-2
- Module(s): Module 3.IMT: Specialisation Investment Management Track
- Language: EN
- Mandatory: Yes
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Course learning outcomes
• Deal with state-of-the-art methodologies that underpin professional portfolio management • Have a thorough understanding of the core aspects of the investment management process, from the perspective of the individual investor, the corporate financial manager, and the investment manager• Have a deep understand of the following three main blockso Assets and Portfolios (“why, who, what, where?”): the goals of investors and portfolio managers, overview of asset classes, practical aspects of trading. o Portfolio Construction (“how?”): determining the investment policy, devising investment strategieso Portfolio Management (“when?”): dynamic performance evaluation and portfolio monitoring, life-cycle and tax-efficient investingo Be acquainted with current topics, such as socially responsible investing, FinTech, and ethics in portfolio management -
Description
1. Assets and Portfolios- The investment process: Introduction and Overview of Asset Management- Trading: How do security markets work? 2. Portfolio Construction- Asset Allocation: review on mean variance analysis, practical aspects- Portfolio Choice and Security Prices: CAPM Review, Asset Pricing Models- Modern Asset Allocation: Multifactor Models, Anomalies, Factor Investing3. Portfolio Management- Performance Measurement and Monitoring- Life-Cycle and Tax-Efficient Investing4. Current Topics- Socially Responsible Investment- Fintech- Ethics in Portfolio Management o Why Ethics Matters to the Investment Industryo Standard I: Professionalismo Standard II: Integrity of Capital Marketso Standard III: Duties to Clientso Standard IV: Duties to Employerso Standard V: Investment Analysis, Recommendations, and Actionso Standard VI: Conflicts of Interest -
Assessment
Assessment Modality
Combined or continuous assessment
Assessment Tasks
Type of Assessment
Grading Scheme
Weight for final Grade
Task 1
Other, please specify
Report
0-20
60 %
Specific Assessment Rules
N/A
Task 2
Presentation
0-20
30 %
Specific Assessment Rules
N/A
Task 3
Active participation
0-20
10 %
Specific Assessment Rules
The course requires regular attendance and participation in class.
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Note
Literature:
• Bodie, Z., A. Kane, and A. J. Marcus, Investments, 2014, 10th ed., McGraw-Hill/Irwin, New York. [BKM]• Ang, A., Asset Management: A Systematic Approach to Factor Investing, 2014, Oxford University Press. [AA]
An entirely self-contained set of lecture notes (slides) will be distributed. These will cover all the course relevant material in detail. Students are strongly encouraged to complement their background reading with textbook materials detailed below.
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Details
- Course title: 3.IMT3.Investment Strategies
- Number of ECTS: 5
- Course code: MScFE_IM-3
- Module(s): Module 3.IMT: Specialisation Investment Management Track
- Language: EN
- Mandatory: Yes
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Course learning outcomes
• Understand the key characteristics of active and passive investing and the features that investment strategies of each type have in common• Identify and characterize core investment strategies of UCITS funds • Understand and be able to implement well-known investment strategies • Have an understanding of investment strategies’ potential pitfalls and practical restrictions due to transaction costs, liquidity, risk management and regulatory constraints. • Present their investment strategy to potential investors -
Description
The aim of this course is to shape students’ understanding of the process of investing in public capital markets. It explores popular investment strategies from the perspective of the portfolio manager of a UCITS fund: indexing, value investing, momentum investing, macro strategies, ESG investing, activist strategies, systematic strategies etc. In addition, the practical considerations related to portfolio management, order execution, and fund risk management are covered. At the end of the course, students give their fund pitch presentations.1. Active vs. passive investing- Rise of passive investing- Fee compression- Other major market trends2. Capital market instruments and regulated markets – Eligible assets:Money market instruments, bonds, equities and currencies- OTC and ETD derivatives – Cash management- Eligible assets- Stock exchanges and other regulated markets3. Passive Investing- ETF vs. index fund- Passive strategies: Buy & hold indexing etc.- Securities lending4. Active Investing- Fundamental analysis- Technical analysis, momentum, reversal, calendar effects- Stock characteristics, value vs. growth- Event-driven strategies – Tactical asset allocation- Active fixed income strategies5. Earnings and activism- Value added, misvaluation, corporate governance- Activist funds- Campaigns and proxy fights- Shareholder rights and investment funds6. ESG and sustainable finance- ESG, sustainability risk integration, Impact investing and socially responsible investing- EU taxonomy, SFDR7. Macroeconomic data and Investment strategies- Macroeconomic indicators – Global macro strategies, CTA / Managed futures- Currencies Investing: momentum, value and carry trade8. Systematic strategies- Factor investing: macro and style factors- Smart beta- AI strategies9. Portfolio management in practice- Transaction costs – Best execution- Liquidity considerations- Leverage, collateral management- Efficient portfolio management techniques- KID and prospectus10. Risk management of investment funds- Market risk, credit risk, counterparty risk, concentration risk, liquidity risk- UCITS Investment Restrictions- Global Exposure: Commitment approach vs. Value-at-Risk- Stress testing and liquidity stress testing11. Fund pitch presentations -
Assessment
Assessment ModalityCombined or continuous assessmentAssessment TasksType of AssessmentGrading SchemeWeight for final GradeTask 1Written exam 0-2050 %Specific Assessment Rules1.5 h Task 2Presentation 0-2030 %Specific Assessment RulesN/A Task 3Other, please specify Choose an item.20 %Specific Assessment Rulespractical exercises (KID, fund strategy description) -
Note
Literature:A set of articles will be provided during the course.
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Details
- Course title: 3.IMT4.Alternative Investments
- Number of ECTS: 5
- Course code: MScFE_IM-4
- Module(s): Module 3.IMT: Specialisation Investment Management Track
- Language: EN
- Mandatory: Yes
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Course learning outcomes
• Have an in-depth understanding of the alternative investment industry and be able to characterize and understand the common features of alternative investments such as hedge funds, private equity / venture capital funds, infrastructure, real estate and commodities• Recognize and distinguish the specific organizational and operational setup of the major asset classes of alternative investments, their particular investment strategies and investor profiles• Understand and be able to implement known investment strategies of hedge funds, venture capital funds, buyouts, infrastructure funds, and commodities; have a good knowledge of strategies’ evaluation, risk management and risk-return profile• Have a thorough understanding of how alternative investments can be incorporated into an investment portfolio from an asset allocation perspective; be able to assess the benefits and pitfalls of introducing alternative investments in a portfolio • Have a grasp on markets for alternative investment: how to access them through private placements to registered products -
Description
The course provides students with a thorough overview of various forms of alternative investments, such as hedge funds, private equity and venture capital funds, real estate and commodities. Its purpose is to give students a good understanding of how alternative investments operate and of the underlying investment strategies.In particular, it aims at equipping students with knowledge of alternative investment strategies (both directly and through funds of funds), methods to evaluate their performance and valuation approaches, the operational and organizational aspects of alternative investment vehicles and their legal environment. The class is organised as follows:1. Overview of Alternative Assets- The alternative investments industry- Key characteristics of alternative investment vehicles (hedge funds, private equity and venture capital funds, managed futures, commodities, real estate) and investor profiles- Operational and organizational structures- Vehicles valuation on regulated and non-regulated markets2. Hedge Funds- Establishing a hedge fund investment program- Hedge fund strategies- Benchmarks and asset allocation- Evaluating hedge fund performance and risk management- Funds of Funds3. Private Equity- Venture Capital- Leveraged Buyouts- Performance Measurement- Valuation of Investments- Exit Strategies- ESG in Private Assets4. Risk Management for Alternative Investment Funds- Differences between traditional and alternative investment funds- Regulatory overview- Market, liquidity, and operational risk- Risk reporting -
Assessment
Assessment Modality
Combined or continuous assessment
Assessment Tasks
Type of Assessment
Grading Scheme
Weight for final Grade
Task 1
Written exam
0-20
40 %
Specific Assessment Rules
2 h
Task 2
Other, please specify
0-20
40 %
Specific Assessment Rules
Seminar paper
Task 3
Presentation
0-20
20 % -
Note
Literature:
Recommended background reading:Mark J.P. Anson, Handbook of Alternate Assets. Second Edition. John Wiley & SonsAndrew Lo, Hedge Funds: An Analytic Perspective, Updated Edition
A self-contained set of slides and case studies will be published on Moodle prior to the start of the course. Students are strongly encouraged to supplement their reading by articles listed on the course slides.
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Details
- Course title: 3.IMT5.Time series analysis
- Number of ECTS: 5
- Course code: MScFE_IM-5
- Module(s): Module 3.IMT: Specialisation Investment Management Track
- Language: EN
- Mandatory: Yes
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Course learning outcomes
• Independently apply the presented methods to real empirical problems. To do so, they will have to hand in a series of assignments in which they deal with real economic and financial data.• Clearly understand the econometric tools that are essential to study economic and financial data• Independently apply these tools to real time series data• Critically assess the suitability of the presented methods given the characteristics of real time series data -
Description
Financial time series and their characteristics• Stylized facts• Important concepts: stationarity, autocorrelation, etc.• Applications of time series modelsRegression analysis for time series: the stationary case • Static and dynamic regression models• Inference• Forecasting using dynamic modelsARMA• Estimationo Maximum likelihood estimationo Model choice• Forecasting• Extension: VAR modelRegression analysis for time series: the non-stationary case • Deterministic trends• Stochastic trends• Unit root tests• CointegrationVolatility models• Data and stylized facts• GARCH models• Estimation• Forecasting• Value-at-Risk, Expected Shortfall • Extension: multivariate volatility models -
Assessment
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
2 h
Task 2
Other, please specify
Choose an item.
40 %
Specific Assessment Rules
Assignments
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Note
Literature:
• Campbell John, Lo Andrew, and McKinlay Craig (1997), The Econometrics of Financial Markets, Princeton University Press• Hamilton James D. (1994), Time Series Analysis, Princeton University Press• Harvey Andrew (1990), The Econometric Analysis of Time Series (second edition), Philip Allan• Tsay Ruey S. (2010) Analysis of Financial Time Series (third edition), John Wiley & Sons• Wooldridge Jeffrey M. (2013), Introductory Econometrics: A Modern Approach (fifth edition), Thomson South-Western
All relevant course material will be provided in lecture notes (slides). Command of statistical software like Matlab, R, Stata, or Eviews is required.
Course offer for Investment Management, Semestre 4 (2024-2025 Summer)
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Details
- 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
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Objectives
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.
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Description
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. -
Assessment
60% Seminar paper30% Presentation10% Discussion -
Note
Academic articles that represent the key references for the set of research projects distributed ahead of the course.
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Details
- 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
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Course learning outcomes
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. -
Description
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? -
Assessment
2 hours written exam during the course period -
Note
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.
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Details
- 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
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Objectives
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
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Description
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/ -
Assessment
Examen écrit de 2 heures pendant la période de cours -
Note
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
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Details
- 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
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Objectives
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
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Description
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 -
Assessment
The final grade will be an oral presentation (100%) -
Note
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.
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Details
- 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
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Objectives
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.
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Course learning outcomes
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Description
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. -
Assessment
Seminar Paper (95%)Active participation (5%) -
Note
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.
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Details
- Course title: 4.Applied Master Thesis (including Internship)
- Number of ECTS: 20
- Course code: MScFE_BK-19
- Module(s): Module 4.IMT.Master Thesis
- Language: EN
- Mandatory: No
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Details
- Course title: 4.Academic Master Thesis
- Number of ECTS: 20
- Course code: MScFE_BK-20
- Module(s): Module 4.IMT.Master Thesis
- Language: EN
- Mandatory: No