Financial Markets - 203 - 2nd year of master's degree

Ects : 3

Lecturer :
SYLVAIN BENOIT

Total hours : 24

Overview :

The objective of the course is to study the theory, modeling, programming, and interpretation of the major time series models. Some applications to finance will be undertaken using Python. At the end of this class, students should be able to :

• Develop knowledge of basic univariate time series models appropriate for economic and financial data.
• Learn how to specify and estimate a time series model on these data.
• Be able to use such models for forecasting and to evaluate their performance.
• Familiarize with common volatility modelling approaches.

Course outline:

1/ Time series building blocks

• Stationarity
• Autocorrelation and white noise
• Testing autocorrelation
• Non-stationarity
• Python exercices

2/ ARMA Framework

• Moving average process
• Auto regressive process
• ARMA models and the Box-Jenkins method
• Maximum-Likelihood estimation
• Simulation and model selection with Python

3/ Specific topics and applications

• Unit-roots
• Trends
• Seasonnality
• Python application to the Earnings-Per-Share

4/ Volatility models

• GARCH
• Value-at-Risk
• Expected Shortfall
• Yahoo-Finance API and ARCH/GARCH modelisation with Python

5/ Principal Component Analysis

• Normed vs. Non-normed PCA
• Contribution and quality of representation of observations and variables on principal components
• Computation of the Absorption Ratio with Python

Coefficient : 1.5

Require prerequisites :

Students must be enrolled in course Python Programming and must have past Introduction to Financial Econometrics.

Learning outcomes :

Master the econometrics (dynamic) tools used in empirical finance

Assessment :

Assignment (30%) + Final Exam (70%)

Bibliography-recommended reading

Brooks C (2008), Introductory econometrics for Finance, Cambridge Univ Pr. Brockwell, P.J. and Davis, R.A. (2002), Introduction to time series and forecasting, Springer Verlag. Campbell J., Lo A., McKinley, A. (1997), The Econometrics of Financial Markets. NJ: Princeton University Press. Francq C, Zakoïan J.M. (2010), Garch models: Structure, statistical inference and financial applications, Wiley. Hamilton J. D. (1994), Time Series Analysis, Princeton University Press.

Ects : 6

Lecturer :
JULIEN CLAISSE

Total hours : 24

Overview :

The aim of this lecture is to present the theory of derivative asset pricing as well as the main models and techniques used in practice. The lecture starts with discrete time models which can be viewed as a proxy for continuous settings. We then develop on the theory of continuous time models. We start with a general Itô-type framework and then specialize to different situations: Markovian models, constant volatility models, local and stochastic volatility models. For each of them, we discuss their calibration, and the valuation and the hedging of different types of options (plain Vanilla and barrier options, American options, options on realized variance,...).

Course outline:

I. Discrete time modelling I.1. Financial assets I.2. The absence of arbitrage I.3. Pricing and hedging of European options I.4. Pricing and hedging of American options

II. Continuous time modelling II.1. Financial assets as Itô processes II.2. The Black-Scholes model II.3. Markovian models in complete markets II.4. Local volatility models II.5. Stochastic volatility models

• General setting
• Tree markets
• Risk-neutral measures
• Fundamental theorem of asset pricing
• The super-hedging problem
• The complete market case : example of the CRR model
• Approximate hedging in incomplete markets
• Examples: binomial and trinomial tree markets
• The Itô process framework
• Discussion of the Absence of arbitrage opportunity
• Complete and incomplete markets
• The general pricing and hedging principle for European and American claims
• Characterization of complete Black Scholes markets
• Explicit formulas : European call option (Black-Scholes formula), barrier option (reflection principle)
• PDE valuation (plain vanilla, barrier, Asian, American options
• Greeks and hedging
• Tracking error and convexity
• Dupire’s formula and calibration to the volatility surface
• Su per hedging prices
• Completion of the market with options : general principle, Approximate static hedging: example of the variance swap hedging problem
• Specific models : CEV, Heston, SABR,...

Coefficient : Coefficient 1 (M2 Research in Finance) Coefficient 3 (M2 Financials Markets)

Require prerequisites :

Students must have past Financial Derivatives and Derivative Pricing& Stochastic Calculus 1.

Learning outcomes :

The lecture starts with discrete time models which can be viewed as a proxy for continuous settings, and for which we present in detail the theory of arbitrage pricing. We then develop on the theory of continuous time models. We start with a general Itô-type framework and then specialize to different situations: Markovian models, local and stochastic volatility models. For each of them, we discuss the valuation and the hedging of different types of options : plain Vanilla and barrier options, American options, options on realized variance, etc. Finally, we present several specific volatility models (Heston, CEV, SABR,...) and discuss their specificities.

Assessment :

Final exam

Bibliography-recommended reading

Bouchard B. et Chassagneux J.F., Fundamentals and advanced Techniques in derivatives hedging, Springer, 2016. Lamberton D. et B. Lapeyre, Introduction au calcul stochastique appliqué à la finance, Ellipses, Paris, 1999.

Ects : 3

Lecturer :
GUILLAUME MONARCHA

Total hours : 24

Overview :

The last ten years have seen an extraordinary growth in the use of quantitative methods in financial markets. Professionals now use sophisticated statistical techniques in portfolio management, proprietary trading, derivative pricing, risk management and securities regulation. This course has two main objectives. The first one is to offer an overview of mostly used econometrics tools, and some of their developements in the machine learning area: moment estimation, linear factor models, dynamic linear models, latent factor models, numerical simulations, model selection, clustering. The second one is to highlight the strong link between academic research and their practical implementation in various fields – portfolio construction, asset pricing, fund analysis, performance evaluation, quantitative investment strategies, factor investing, backtesting – through the analysis of research papers and applications into Python.

Course outline: Lecture 1 - An Overview of Financial Data

• The statistical properties of financial asset’s return
• Portfolio returns and aggregatio
• Distribution estimation
• Robust estimator
• Distributional test
• Time dependency

Python applications: distributional tests, modified-/conditional-/theoretical value-at-risk estimations. Lecture 2 - Econometrics of the Efficient Frontier, part 1

• Theoretical Background of the efficient frontier and Sharpe ratio estimation
• The impact of estimation errors on efficient portfolios, and more generaly on portfolio optimizations

Python applications: simulation of estimation errors ; illustration of the impact of estimation errors on optimal porfolio weights. Lecture 3 - Econometrics of the Efficient Frontier, part 2

• Portfolio optimization in practice
• Dealing with the presence of estimation errors in the estimation of the efficient frontier
• Bootstrap estimation of the efficient frontier
• Testing the efficiency of the efficient frontier and efficient portfolios

Python applications: Replication of the main results of 3 research papers (cf. references): simulation of statistically equivalent optimal portfolios, estimation of the resampled efficient frontier, bootstrap estimation of the efficient frontier. Lecture 4 - Factor Pricing Models

• From the CAPM to multifactor model
• The main types of factor models
• Latent factor models
• Financial applications and paper study (cf. references)

Python applications: Identification of the cross-sectional return drivers of global macro hedge funds. Lecture 5 - Dynamic factor Models

• Conditional factor models
• Dynamic factor models
• Dynamic latent factor models

Python applications: estimation of fund dynamic exposures, implementation of trend following strategies. Lecture 6 - Model selection

• Stepwise selection methods
• Ridge and LASSO regressions
• Statistical inference and cross validation

Python application: Identification of the global macro factors driving equity returns. Lecture 7 – Backtest validation

• Backtesting biases
• Assessing the impact of backtesting bias on return distributions
• Estimating backtest parameters
• Validating backtested strategies

Python applications: backtesting the momentum alternative risk premia strategy.

Coefficient : 1.5

Learning outcomes :

Master econometrics (static) tools in empirical finance: factor models, risk premia, etc.

Assessment :

Final Exam

Bibliography-recommended reading

• Greene W. H., 2017, “Econometric Analysis”, 8th edition, Pearson, 1176 pages.
• Hastie T., Tibshirani R., and J. Friedman, 2016, “The Elements of Statistical Learning”, Springer Series in Statistics, 2nd edition, 745 pages.
• Meucci A., 2009, “Risk and Asset Allocation”, Springer Finance, 3rd Edition, 532 pages.
• Tsay R., 2010, “Analysis of Financial Time Series”, Wiley-Blackwell, 3rd edition, 712 pages.

Ects : 3

Lecturer :
JACOPO D'ANDRIA
CONSTANCE BOUBLIL-GROH

Total hours : 21

Overview :

The objective of this course is to describe the strong interactions between financial markets and the economy:

• from the economy towards markets, by reviewing how economic newsflow affects the fundamental valuation of different assets classes,
• from markets towards the economy, by presenting the predominant role played by financial markets and financial institutions in the economy (crises, raw materials price movements,.)

The course will be organized around different themes prevalent in the current financial and economic environment considered as helpful to illustrate fundamental financial and economic principles. Particular attention will be drawn on the links between finance and economic policies, as fiscal policy, monetary policy and finance regulation.

The course will go through the following themes: . Session I - Reminder of key macroeconomic analysis tools (1.5h – Alain) · Session II - Global imbalances and financial implications (including portfolio allocation) (1.5h – Alain) · Session III - Interaction between finance, fiscal and monetary policies with a specific focus on the US (2h – Alain) · Session IV- Interaction between finance, fiscal and monetary policies with a specific focus on the Eurozone (2h – Alain) · Session IV- Between Theory and Evidence: Case studies illustrating the application of tools in previous sessions (2h - Alain) · Session V – Economic developments in emerging markets (3h – Constance) · Session VI – Implications of key financial crises in emerging markets and application through case studies (3h – Constance) · Session VII – Evolution of the financial regulation through crises (3h – Simon) · Session VIII – Implication for the banking sector and case studies (3h – Simon)

Coefficient : 1.5

Learning outcomes :

Master financial en economics environement as well as the interactions between financial markets and the economy

Assessment :

Final Exam  

Bibliography-recommended reading

Frederic S. Mishkin « Economics of Money, Banking, and Financial Markets » Carmen M. Reinhart & Kenneth S. Rogoff « This Time Is Different: Eight Centuries of Financial Folly »

Ects : 3

Lecturer :
ARNAUD ANGELO
LAURENT DAHAN

Total hours : 30

Overview :

Part 1 (L. DAHAN – 18h) : Risk management in practice The Part 1 objective is to understand the issue of risk management. The principal ways of understanding and learning about risk are considered. Greeks computation is used to explain the daily P/L in a normal trading environment. The VaR is used to take into account market tail events while stress testing highlights performance under extreme market conditions. In a similar measure the course attempts to present the counterparty risk for market operations. This course shows also how all these measures come within the framework of the Basel Accords (3 pillars, regulatory capital requirements...) Part 2 (X. BOCHER – 12h) : Mathematical framework of market risk measures and its limits The Part 2 objective is to go deeper into Risk Measures introduced in Part 1, from a mathematical prospective (main measures, properties, limits, implementation). Then the course leads to the identification of main empirical sources of market risks and introduces more sophisticated models that allow to take into consideration those sources of risks. Within this framework, topics will be firstly introduced through empirical observation of data ("Stylized Facts") to lead to modeling answers and their application for risk management purpose.

Course outline: Part 1 : Risk management in practice

• Introduction to Risk-Management
• Value-at-Risk
• Stress test
• Counterparty risk on derivatives

Part 2 : Mathematical framework of market risk measures

• Introduction
• Mathematical definition and properties of a Coherent Risk Measure
• Stylized facts on market risk and models to take them into consideration

Coefficient : 1.5

Learning outcomes :

Master the issues and tools in risk management

Assessment :

Final Exam

Bibliography-recommended reading

Cherubini U., E. Luciano and W. Vecchiato, 2004, Copula Methods in Finance, Wiley, 310 pages. Roncalli T., 2009, La Gestion des Risques Financiers, Economica (2ème édition), Collection Gestion, 455 pages.

Ects : 3

Lecturer :
JUAN FELIPE IMBET JIMENEZ

Total hours : 24

Overview :

This intermediate-level Python programming course is designed to deepen your understanding of Python and enhance your programming skills for a career in quantitative finance. After introducing the foundational concepts of the Python programming language, this course delves into more advanced topics and techniques. You will learn about object-oriented programming (OOP) principles and advanced data structures, as well as essential computer science concepts to profile and write more efficient code. The course will also enable you to write more elegant code, maintain code written by others, and take advantage of the growing popularity of LLMs to code faster. Through practical hands-on exercises, you will develop the ability to design and implement complex Python applications while also gaining proficiency in utilizing external libraries and modules. By the end of this course, you will be equipped with the skills necessary to tackle more challenging programming tasks and create robust and maintainable Python programs.

Program

* Week 1 Introduction to Python, grammar, syntaxis, history, binary arithmetic, good-practices, basic data-structures. * Week 2 NumPy, Matplotlib, Pandas. * Week 3 Parallel programming - Polars and Dask * Week 4 Object Oriented Programming * Week 5 Algorithm Analysis and Numerical Optimization (SciPy) * Week 6 Webscraping and APIs * Week 7 Calling C code from Python * Week 8 LLMs and Code profiling.

Coefficient : 1.5

Learning outcomes :

Knowledge in Python programming for career in quantitative finance

Assessment :

Final Exam 70 % - Class Projects 20 % - Attendance (10%).

Recommended prior knowledge Basic concepts of programming, statistics, linear algebra and convex optimization.

Bibliography-recommended reading

Mandatory literature: Mandatory installation: Python 3.9 and other pydata libraries from Anaconda: https://www.anaconda.com/distribution/

An IDE like VSCode to run python code code.visualstudio.com

Pre-requisite: Recommended material if the student has no experience coding: 1 hour Python beginner tutorial - See the vide

• Hilpisch, Yves, Python for Finance: Analyze Big Financial Data, 2015, O’Reilly Publishing
• Lecture Notes and Github code of the class

Ects : 3

Lecturer :
ALI ARBOUN

Total hours : 21

Overview :

This practical oriented course focuses on learning C++ language as a practical tool. It aims to be both an introduction to C/C++ and a basic course for whoever want to get an expertise in programming. A special care to practice is taken through solving simple issues C++ as a tool ; no special programming background is expected. The 2 last courses are dedicated to advanced topics, not mandatory to learn the language, but important for people with programming background especially C programmers. A good grasp of the previous lessons will be required.

Course outline: 1. The basics

• Working with git
• C++ first program, compilation chaintools, input and output
• Variables and types, scopes, operators
• Working with numbers: maths VS computers
• Control structures

2. Express your algorithms using C++

• Functions (declaration, arguments, overloading)
• lvalue and rvalue references
• Namespace
• Precompiler and macros
• Program structure and build process
• Application: closed-formulae for pricing

3. Programming with the STL

• Sequential containers
• Associative containers
• Algorithms, iterators, functors
• Lambdas
• Streams

4. Defining your own types

• Structures and classes
• Value semantic
• Conversions
• Operator overloading
• Application: data models in pricing libraries

5. Managing memory and low-level data structures

• Pointers and arrays
• Pointer to method and functions
• Three kinds of memory
• Exceptions

6. Making your types abstract

• Inheritance and polymorphism
• Entity semantic and Liskov Substitution Principle
• Runtime Type Information
• Multiple inheritance
• Inheritance of implementation
• Application: reusable numerical tools

7. Generic programming: write less, do more

• Template classes and speciali zations
• Template functions, specialization vs overloading
• Type deduction and auto
• Universal references and perfect forwarding
• Curiously Recurring Template Pattern
• Traits
• Managing the overload resolution set
• Variadic templates and advanced metaprogramming

Coefficient : 1.5

Learning outcomes :

Knowledge in C++ programming for finance

Bibliography-recommended reading

Beginners: Stanley B.Lippman, Josee Lajoie, Barbara Moo, "C++ Primer ", Fifth Edition, 2012. Koenig A. & B. E. Moo, "Accelerated C++", Addison-Wesley, 2000 Reference guides: Bjarne Stroustrup, "The C++ Programming Language", Fourth Edition, 2013. Nicolai M. Josuttis N. M., "The C++ standard library" 2nd edition, Addison-Wesley, 2012 Scott Meyers, “ Effective STL ” , Addison-Wesley, 2001

Online Gurus: www.drdobbs.com

Ects : 3

Lecturer :
GUILLAUME LEENHARDT

Total hours : 21

Overview :

To share with students the combined, yet distinct, importance of economics and geopolitics in the shaping of the global energy map, and how these can in turn trigger long term effects globally on both society and economy as a whole. The course will also elaborate on the recent "financiarisation" of the energy sector and its related impact.

Course outline: The course starts with a review of the key historical periods of the energy industry and the progressive shaping of today's energy sector, especially for the period starting from the decolonisation to today's resource nationalism period. It will focus on the evolution of the global supply/demand equation, its specificities for the major energy resources as well as its relative impact (together with that of economic theory) on resource valuation. It will then elaborate on the major changes in the recent shaping of the energy industry, with the emergence of new Major energy firms, the quest for ever more difficult production areas and technologies and the role of pioneer oil companies. Throughout the course, the ever-growing involvement of the financial industry in the energy sector will be highlighted. The course will then invite energy sector specialists to share with students their experience and views on some of the key aspects and issues:

• the investment and financing decision in the upstream oil sector
• the emergence of gas and LNG and its ever changing global dynamics
• will shale oil and gas reshape the global energy map?
• the emergence and role of international energy traders and banks in the supply chain
• resource Narionalism in today's complex geopolitical environment
• the growing and essential role of regulation throughout the energy sector
• renewable energies

Coefficient : 1.5

Learning outcomes :

Knowledge of the history and evolution of the energy sector and its main drivers.

Ects : 3

Total hours : 18

Overview :

• Introduction to Mergers & Acquisitions' issues and practice,
• Introduction to valuation methods and M&A process,
• Presentation of usual transaction structures with a special focus on tender offers, takeover bids, structured finance, and LBO transactions.

Course outline:

Class #1: Introduction to M&A

• The ecosystem of M&A (landscape of parties involved): types of transactions, framework of an M&A process, types of investors,
• For M&A, 1+1 = 3: value creation,
• Course based on Group Flo transaction: €300 million, 155 restaurants,
• How to conduct a transaction from the beginning to a final tender offer.

Class #2: Basic valuation and structure

• What’s the difference between Price and (intrinsic) Value,
• The language used in finance is accounting: = basic accounting, how to read financial statements,
• Standalone financial statements vs. Consolidated accounts,
• “Qui-est ce ?” Can you identify the activity of a company using its P&L & B/S?(Exercise used at H.E.C.),
• France Pare Brise: financial statements of a company in a distressed situation. Are you capable of identifying the decisions that need to be made to save the company?
• Valuation methods (transaction multiples, trading multiples, DCF, DDM, LBO models, NAV),
• The course is Case study based.

Class #3: Buy-side versus sell-side M&A

• Description: who do what?
• Review of an HSBC pitch book prepared for Buffalo Grill,
• Overview of the timetable,
• Differences in modelling,
• How to purchase a company? How to structure the acquisition,
• Most common calculations (premium, FDSO, transaction values, transaction multiples …)
• Example of Provimi, a company worth €1,4 billion: review of the business plan used in the transaction (which dates back 2008), and the fairness opinion (invoiced for €200.000 for 33 pages report).

Class #4: Excel Models, business planning, auditing of a spreadsheet

• Key principles,
• Why do bankers create Business plans, and how?
• How to enlighten the key considerations on a model,
• Are assumptions realistic? Why?
• What kind of stress, and which “haircut” will the banker use to test the resistance of financial forecasts,
• Course based on very many different excel spreadsheets used in real transactions.

Class #5: Takeover Bids and LBO transactions

• Key principles
• Friendly vs. hostile bids,
• Case study: the acquisition of Gilette by Procter&Gamble,
• Rationale of “leverage”, and the relationship between leverage, risk and I.R.R.
• Case study: the I.P.O. of Mr. Bricolage,
• The recent considerations on Structured finance & Leveraged Buy outs: Debt Push Down (How to relocate debt at the level of the assets rather than at the level of the holding company)

Class #6: Negotiations

• Key principles,
• Structuring and modeling a transaction. But negotiating is key,
• What are the main issues at stake,
• How to bridge a valuation gap (earn-outs, price adjustments),
• Case study based on legal documentation for a transaction,
• Possible case study from Harvard Business School: “Café Kenya”.

Coefficient : 1.5

Learning outcomes :

Master usual transaction structures with a special focus on takeover bids and LBO transactions.

Assessment :

- 45min multiple-choice questionnaire - No documents allowed - 1 case study

Bibliography-recommended reading

• Vernimmen P., P. Quiry et Y. Le Fur, 2016, Finance d'entreprise, 14th Edition, Dalloz, 1124 pages.
• Filippelli M. A., Mergers and Acquisitions Playbook: Lessons from the Middle-Market Trenches, 2010, 1st Edition, ? Wiley, 338 pages.
• Higgins R., ISE Analysis for Financial Management, 2022, 13th Edition, McGraw-Hill Education, 464 pages.
• Reed S. F., A. Lajoux, and H. P. Nesvold, The Art of M&A: A Merger Acquisition Buyout Guide, 2019, 5th Edition, McGraw-Hill Education, 1264 pages.
• Rosenbaum J. and J. Pearl, Investment Banking: Valuation, LBOs, M&A, and IPOs, 2020, 3rd Edition, Wiley, 512 pages.

Ects : 3

Lecturer :
THIBAULT GODBILLON

Total hours : 21

Overview :

Banks, and the financial sector more broadly, operate in a highly regulated environment. Financial regulations have evolved over time, in response to key events, such as the 2008 Global Financial Crisis, emerging risks (e.g., data, cyber security, FinTech, etc) and more recently the COVID19 pandemic, or the failure of SVB and Credit Suisse. Regulations have broadened to encompass all parts of the financial system: banks and non-banks — insurers, market infrastructures, credit rating agencies, hedge funds, etc. Global policymakers (including BCBS, FSB, IOSCO) have developed international standards to support the G20 mandate — ensuring the stability and resiliency of the global financial system. At the local and regional level (in the EU for instance), prudential and market regulators are tasked with transposing these global standards in their own framework, which may cause some variations in the way regulations are implemented across jurisdictions. This lecture aims to provide students with an understanding of the global regulatory architecture, ensure they understand where regulations come from, and how to stay up-to-date with a complex and constantly evolving topic. The course will also provide students with an overview of the current rules and regulations applying to banks and financial market operators in general. Via the drafting of a two-page note on a specific topic from the course, students will practice their written English communication and capacity to summarise complex matters. Finally, via the participation of experts from various background, the course will provide students with an insight into working for global organisations.

Course outline:

1) An introduction to financial regulations 2) Prudential regulations (Basel standards, CRD/CRR, DFA) 3) Crisis management (FSB standards, BRRD/CMDI, DFA) 4) Overview of Market regulations (International standards, MIFID/EMIR) 5) Sustainable Finance (Key risks, FSB/BCBS stan dards, EU taxonomy/GBS/ SFDR) 6) Digital Finance (Key risks, FSB/BCBS standards, DORA/MiCAR) 7) Outro (Wrap-up, critical considerations on financial regulations)

Coefficient : Coefficient 1 (M2 Research in Finance) and Coefficient 1.5 (M2 Financial Markets)

Learning outcomes :

Master the regulatory prudential and market reforms, at the global level and across regions

Assessment :

Each students will be asked to prepare a two page note aimed at summarising a key issue of the programme.

Ects : 3

Lecturer :
BRICE PERIN
SCHLOMY BOTBOL

Total hours : 21

Overview :

The objective of the course is to give in-depth knowledge about Volatility Trading Strategies. The goal is to provide participants with the various uses of volatility, its dynamics, the instruments to trade it, the risks embedded in and how to avoid classic pitfalls about volatility.

We will cover main instruments to trade Volatility (from vanilla options to more complex products as Conditional Variance swaps or Vix Options) and how to manage them in the various volatility strategies (Vega/Gamma Trades, Relative Value Trading, Risk Overlay, Cross-Asset volatility arbitrage strategies, Convertible Bonds Arbitrage, Variance Trades, Dispersion…).

Course outline: The course (4 Courses Sessions and 3 Lab Sessions) will go through the following themes:

• Vanilla Options Trading Strategies and associated risk/returns
• Multi-Leg Option Strategies
• Volatility Investment Solutions
• Dynamic Hedging of associated Risks
• Risk Overlay for Long Equity portfolios
• Cross Asset Volatility Arbitrage Strategies
• Convertible Bonds Arbitrage
• Volatility Swaps and Variance Swaps Dynamics
• Variance Swaps Replication
• Correlation and Dispersion Trading
• Vix : Dynamics and related products

Coefficient : 1.5

Learning outcomes :

Practical knowledge to design quantitative investment strategies.

Bibliography-recommended reading

Bennett C. (2012), Volatility Trading, Santander Research Notes Cochrane, J.H.(2005), Asset Pricing, Revised Edition, Princeton University Press. Demeterfi, Derman, Kamal & Zou, (1999), More Than You Ever Wanted to Know About Volatility Swaps, GS Research Notes Hull, J. (2006), Options, futures and other derivatives, 6th ed., Pearson Prentice Hall Ilmanen, A. (2011), Expected returns, Wiley Finance. MacDonald R. L. (2006) Derivatives Markets, 2nd ed., Addison Wesley Riva, F. (2008) Applications financières sous Excel en Visual Basic, 3ème éd., Economica. Taleb, N. (1997) Dynamic Hedging: Managing Vanilla and Exotic Options, Wiley

Ects : 3

Lecturer :
PHILIPPE NARDONE
VINICIUS WOLF
NICOLAS RAYMOND

Total hours : 24

Overview :

Course objectives

Conducting business in the financial sector means conducting business with highest standards of ethics and in accordance with the laws and regulations of the countries where the business is done. The course’s objectives are · to understand the importance of ethics and professional standards when conducting business in the financial sector; · to get a basic knowledge of the regulation and laws; · to understand the main compliance concepts applied in Corporate & Investment Banks

Part 1. Ethical and Professional Standards

This part offers a pragmatic approach of ethics in finance, pointing out some of the recent issues that emerged since the financial crisis. The course takes as a starting point some of the recent codes of conduct issued by the finance industry as well as CFA Institute® Code of Ethics and Standards of Professional Conduct; it then turns to concrete issues such as rate-rigging, toxic assets or liabilities, product structuring, investor protection, as well as some of the recent regulation. Topics are covered through presentations in class, student presentations, exercises and case studies. Student presentations are delivered individually, in class, under a pre-set format, and are part of the participation grade. As a prerequisite, students must be familiar with CFA Institute® Code of Ethics and have prepared an example of a standard violation and corrective action for the first class.

Part 2. Global Compliance

Main objectives are giving students a global overview on the main Compliance concepts applied in a Corporate & Investment Bank and emphasizing the latest trends in regulatory environment. Theoretical courses and practical examples will be exposed to students on the main Compliance and Financial Security themes met in a Corporate & Investment Bank. · Compliance: privileged information, information barriers, conflicts of interests, market abuse and insider trading, suitability, reputation risk, etc... · Financial Security: KYC, KYB, and implementation of the European 3rd Directive ; embargos, countries on watch lists, combating money laundering, fraud prevention.

Course outline

Introduction Course: Regulation today - for a better understanding of Ethics and Compliance (3h)

· Evolution of regulation and where we are now · Linkage between the directives · Comparison EU/rest of the world

Part 1. Ethical and Professional Standards

Session 1-Course Introduction: (1h)

Why do ethics matter? How to prepare a presentation, a case study, an exercise? Exercise on Standard violations: (Using CFA Institute® Code of Ethics and Standards of Professional Conduct) Debrief on the example prepared by each student for and before Class 1.

Session 2- What do Codes of ethics and Codes of conducts tell us? (2h)

Compare 2 different codes: what is the focus? How well do they protect clients? other stakeholders? Identify what codes teach us about business ethics, operational risks, reputation risk.

Session 3- FX rate-rigging & other benchmarks (2h)

The FX rate-rigging scandal – FX markets Codes of conduct. Importance of trust in benchmarks.

Session 4- Libor rate-rigging & other benchmarks (2h)

The Libor manipulation scandal–Libor administration before/after the scandal. Regulation on benchmarks and indices.

Session 5- Toxic assets and liabilities (2h)

Navigating through toxic assets and liabilities – examples of debt issued by local authorities, structured loans, structured assets. Workshop on how to “fix” a toxic financial product.

Session 6- Investor protection&Product governance, why does it matter? (2h)

Investor protection is described through: MIFI D II, and examples on ETFs, closet-funds. Product governance is described through: MIFID II, and examples of misspelling.

Session 7- Market related issues (2h)

This session considers ethical and best practices issues with: HFT, insider dealing, illiquid markets.

Part 2. Global Compliance (8h)

I - Course Introduction : DEFINING INTERNAL CONTROL & COMPLIANCE · Why compliance and controls? · Compliance : trying to find a definition · Defining Internal control · Risk mapping methodology II - Financial Security · The European Directives: current impact for banks · Focus on international sanctions : Eurpean Council an US OFAC III - Conduct Risk

III.1 - Market abuse

· Market manipulation · Insider trading · Privileged vs confidential info. · Market abuse prevention rules · From MAD1 to MAD2 · Personal account dealing

III.2 - Reinforcing transparency: focus on the MIFID2 regulation

III.3 - Fight against corruption and fraud

· Fight against corruption (includes new Loi Sapin 2) · fraud prevention

Coefficient : 1.5

Learning outcomes :

Demonstrate ethical awarenesswhen conducting business in the financial sector, as well as the ability to understand the main compliance concepts applied in Corporate & Investment Banks.

Assessment :

Participation and Final exam

Bibliography-recommended reading

Lewis M. , The Big Short, 2011. Flash Boys, 2014 O ’ Malley C. : The story of the Eurobond Markets (ch. 10-11), 2015 CFA Institute® Code of Ethics and Standards of Professional Conduct CFA Institute® Standards of Practice Handbook, 2014 edition

Ects : 3

Lecturer :
KAREN HERRGOTT NICOLAON
NATHALIE COHEN

Total hours : 18

Overview :

Course objectives

“85% of our success accounts from soft skills and emotional intelligence, yet we only pay attention to them 10% of the time."

The result of this study conducted by Stanford university, amongst many others, highlights the importance of increasing our focus on soft skills on the road to professional and personal success. Whether for the purpose of a successful first round HR interview, the fluidity of colleague and client relationships at a first entry job, or the integrity with which one treats themselves and other people - the soft skill module offers you a space of contemplation on emotional intelligence and interpersonal relationships, and its importance in your long term career. This module is not a practical preparation for interviews, but rather a space of reflection on how to know yourself and accept yourself in your strengths and areas of development, so that you may apprehend your interview rounds, your career path, as well as your personal life with integrity, strength and authenticity. "Your level of success will seldom exceed your level of personal development, because success is something you tract by the person you become” - Jim Rohn

Course outline

Part I –Interactive workshops on Authentic Leadership, 1 session of 3h and 6 sessions of 1h30 each. · Emotional intelligence: Group interactive work on emotion & awareness, stress & confidence, emotional intelligence, inter&intra-personal relationships, challenges & opportunites at work · Clarity = Power: Who am I ?, Identifying your Talents and Resources · Emotions Management: Brain plasticity, Face your fears · Communication: Projection and Intuition, Story Telling · Authentic Leadership & Critical Thinking: Ability to Learn & Transmit, Decision-Making Power · Team Work: Flexibility and Collective Intelligence, Win-Win Negotiation · Mindfulness & Positive Attitude: Active Listening, Personal and collective growth

Part II - Discovery and practice of the process of NonViolent Communication according to Marshall Rosenberg over 6 sessions of 1h30 each. · Listening to yourself - Observe one's thoughts & judgements and translate them into needs. - Distinguishing facts/obs. from our interpretations/ judgments · Listening to each other - Offering non-directive listening, with empathic reflection in Feeling & Need - Accepting a difficult message: against oneself, against the other, with oneself, with the other · Dialogue practice - Alternating self-expression and listening to others - Elaborate a solution that considers the needs of each person

Assessment :

No Exam

Bibliography-recommended reading

• Hsieh T., 2010, Delivering Happiness: A Path to Profits, Passion and Purpose, Grand Central Publishing, 255 pages.
• LaLoux F. and E. Lappert, 2016, Reinventing Organizations: Reinventing Organizations: An Illustrated Invitation to Join the Conversation on Next-Stage Organizations, Nelson Parker Ed., 172 pages.
• Rosenberg M. and D. Chopra, 2015, NonViolent Communication: a Language of Life, Puddle Dancer Press Ed., 264 pages.
• Sinek S., 2011, Start with why, Portfolio Ed, 256 pages.
• Tan C.-M., 2014, Search inside yourself, HarperOne Ed., 288 pages.

Ects : 3

Lecturer :
BENOIT GUILLEMINOT

Total hours : 24

Overview :

Commodity markets have experienced exceptional turmoil over the last 20 years. The arrival of new players in futures markets has drastically changed the behavior of commodities prices, connecting them to equity and currency markets. The objective of this course is to provide an economic understanding of the latest developments in commodity markets, grasp the financial, social and regulatory challenges behind commodity investing as well as the necessary concepts and tools to i) evaluate and hedge business exposure to commodities price fluctuations, ii) construct physical or paper trading strategies on commodities markets, iii) price and hedge complex commodity derivatives (on paper contracts or spot price) iv) present risk measurement and stress testing principles for commodity portfolios.

Course outline:

• Introduction
• Spot prices models
• Forward curves
• Options pricing models (Futures options, real and commodity-specific options)
• Commodities investing (properties, indices, relations to other asset classes and hedge funds strategies)
• Commodities dependencies modelling
• Commodities hedging business case: airlines jet fuel supply strategies

Coefficient : 1.5

Learning outcomes :

Deep understanding of the latest developments in commodity markets, grasp the financial, social and regulatory challenges behind commodity investing as well as the necessary concepts and tools

Assessment :

Final exam

Bibliography-recommended reading

Eydeland A., Wolyniec K., Energy and Power Risk Management : New Developments in Modeling, Pricing, and Hedging, WileyEurope (2003). Geman H., Commodities and Commodity Derivatives : Modelling and Pricing for Agriculturals, Metals and Energy, Wiley Finance (2005). Intelligent Commodity Investing, edited by H.Till and J. Eagleeye, Riskbooks (2007). Risk Management in Commodity Markets, edited by H.Geman, Wiley ed (2008).

Ects : 3

Lecturer :
OLIVIER TOUTAIN

Total hours : 30

Overview :

Part 1 : O. Toutain (18h) The objectives of this cours is the following:

• Understanding credit risk
• Mastering the financial mechanisms of CDS and other credit derivatives
• Mastering pricing

Part 2 : F. Astic (12h) This course provides a theoretical and practical analysis of the asset-backed security market. Topics include: Duration And Convexity of Bond Yields, Price Dynamics of Mortgages and Cash Flows, Default Risk, Interest Rate Volatility, Financial Risk Management of Bond Portfolios, Securitization, Corporate Debt And The Securitization Markets, Asset-Backed Commercial Paper, Collateralized Loan Obligations, Structuring Synthetic Collateralized Loan Obligations, Securitization of Revolving Credit, Financial Derivatives And Their Use As Hedging Tools. The course is in the computer lab, where theoretical models are illustrated and solved using Excel. Students will have computer application of topics covered in class using Excel. Students will be assigned a field project, instead of a final exam, that involves financial decision making and real data analysis.

Course outline:

Part 1

1 Credit Risk

• Default risk
• Downgrade risk
• Issuer risk
• Counterparty risk
• Sources of risk
• Characterizing credit risk

2 Measuring Credit Risk

• Ratings: advantages and disadvantages
• Market measures: bond spreads, asset swap spread, CDS spreads

3 Credit Derivatives Markets

• Main products : CDS, Total Return Swaps, Credit Linked Notes, Spread options
• Market size
• Market organization
• Market participants

4 CDS on one entity

• CDS mechanism
• Cash flow diagram
• Buying or selling a protection
• Credit events
• Settlement risks
• Cash settlement / Physical settlement
• Conditions of exercise
• ISDA contract

5 Basket CDS

• Principles : premium of a basket CDS, default correlations, example
• ITraxx : indices, contracts on the iTraxx index
• Les CDOs : generic structure of a CDO, tranching, correlations, Cash CDO and synthetic CDO

6 Pricing of CDS

• Simplified approach for a zero-coupon bond
• Principles of structural models
• products based on a structural model: KMV and CreditGrades
• Principle of intensity models (reduced form models) : extracting default probabilities from CDS spreads

Part 2 Key Structures and Cash Flow Dynamics I. Price Dynamics of Mortgages and Cash Flows

• Bond and Mortgage Basics
• Bond Valuation
• Price/Yield Relationship
• Fixed-Rate Mortgages
• Prepayment Option
• Macauley and Modified Duration
• Convexity
• Risk Exposures

II. Sub-Prime Mortgages, Securitization, The Liquidity problems of August 2007 III. Mortgage-Backed Securities: Origins of the Market

• From the Primary to the Secondary Mortgage Market (The Agency Market,The Private-Label Market)
• Agency and Nonagency Market Segments Compared (Credit Risk Considerations, Mortgage and Funds Flow in the Secondary Market, Industry Illustration)
• Pricing of Newly Originated Mortgages (Freddie Mac Sample Purchase Pricing, Mortgage Pricing from the Bank's Perspective)
• Valuation of Mortgage- and Asset-Backed Securities
• Modeling Cash Flows of Pass-Through, PO, and IO Securities (Information Set, Model, Cash Flow over Time)
• Effective Duration
• Effective Convexity
• Case Study: A Pass-Through Security Issued by FNMA (Prepayment Standard, Assumption Levels, S-Curve Prepayment Function, Weighted Average Life and Different Spreads Measurements, Spread I, Static Spread (Spread Z), and Spread S, Option-Adjusted Spread, Negative Option Cost)
• Case Study: Principal-Only and Interest-Only Securities (PO Strip, IO Strip)

IV. Other Structures in A sset-Backed Securities:

• CMOs, PACs, Floaters, and Inverse Floaters
• Collateralized Mortgage Obligations
• Planned Amortization Class
• Floaters and Inverse Floaters

V. Credit Risk

• Direct Financing of Risk versus Buying a Financial Guaranty
• A Financial Guaranty Is Not an Insurance Contract
• Selecting Efficient Forms and Levels of Credit Enhancement
• Relative Value in Credit-Enhancement Structures

VI. Searching for Value in the Mortgage- and Asset-Backed Markets

• Investment, Speculation, and Hedging
• Target Investment
• Subordinate Segment of Private-Label MBSs
• Interest-Rate Risk (Reinvestment of Cash Flow)
• Interest-Only Strips as a Hedging Tool (Combining Bonds and IOs, Hedging FNMA 9.5 Percent Pass-Through Securities with IO Securities)
• Principal-Only Strips as a Hedging Tool
• Inverse Floaters as a Hedging Tool
• Hedging CMOs with Options on Treasury Bonds

Coefficient : 1.5

Learning outcomes :

Master the mecanisms behind the credit risk products and their pricing models

Assessment :

100% Final exam

Bibliography-recommended reading

Textbooks Options, Futures and Other Derivatives (6th Edition), Prentice Hall, 2005 By John C. Hull Credit Risk : Modeling, Valuation and Hedging, Springer Finance, 2002 By Tomasz R. Bielecki, Marek Rutkowski Articles Altman, Edward, Andrea Resti, and Andrea Sironi, "Default Recovery Rates in Credit Risk Modeling: A Review of the Literature and Empirical Evidence", Economic Notes, Vol. 33, No. 2, (July 2004), pp. 183-208. Jarrow, Robert A. and Stuart M. Turnbull. "Pricing Derivatives on Financial Securities Subject to Credit Risk", Journal of Finance, Vol. L, No. 1, Cornell University, and Queen's University (Canada) (Mar-1995), pp. 53-85. Hull, John and Alan White, "The Impact of Default Risk on the Prices of Options and Other Derivative Securities", Journal of Banking & Finance, Vol. 19, No. 2, (May 1995), pp. 299-322. Duffie, Darrel, Lasse Hefe Pedersen and Kenneth J. Singleton, "Modeling Sovereign Yield Spreads: A Case Study of Russian Debt", Journal of Finance, (February 2003), Vol. LVIII, No. 1, pp. 119-159. Elliott, Robert J., Monique Jeanblanc, and Marc Yor, "On Models of Default Risk", Mathematical Finance, Vol. 10, No. 2, (April 2000), pp. 179-196. Schönbucher, Philipp J., "Term Structure Modelling of Defaultable Bonds", The Review of Derivatives Research, Vol. 2, No. 2/3 (Fall-1998), pp. 161-192. Heath, David, Robert Jarrow, "Bond pricing and the Term Structure of Interest Rates: A Discrete Time Approximation", Journal of Financial and Quantitative Analysis, Vol. 25, No. 4, Cornell University, University of Illinois at Chicago, (December-1990), pp 419-440. Jarrow, Robert A., and Stuart M. Turnbull. "Pricing Derivatives on Financial Securities Subject to Credit Risk", Journal of Finance, Vol. L, No. 1, Cornell University, and Queen's University (Canada) (Mar-1995), pp. 53-85.

Ects : 3

Lecturer :
CLEMENCE ALASSEUR
MARC RINGEISEN

Total hours : 24

Overview :

The purpose of this course is to provide students with an overview of both the technical aspects of energy markets (generation, demand, constraints, market organization) as well as the most commonly used price models for pricing energy derivatives. Attention is given to specific energy derivatives (Swing options and powerplants) and computational methods needed are detailed.

Course outline:

• Introduction to energy markets : electricity and gas market designs
• Price modeling : Spot and Forward models for electricity and gas prices
• Vanilla energy derivatives
• Structured derivatives and physical assets
• Advanced computational methods for stochastic control in energy markets

Coefficient : 1.5

Require prerequisites :

Students must be enrolled in courses Economics and Geopolitics of Energy, Derivatives Pricing& Stochastic Calculus 2, and have past Financial Derivatives, and Derivative Pricing & Stochastic Calculus 1

Learning outcomes :

Good technical knowledge of pricing models and computational methods for energy derivatives products

Assessment :

Final exam

Bibliography-recommended reading

Clewlow L.& Strickland S., Energy Derivatives: Pricing & Risk Management, Lacima Group Pub., 2000. Eydeland A. & Woliniec K, Energy and Power Risk Management: New Developments in Modelling, Pricing and Hedging, Wiley, 2007. Géman H., Commodities and commodity derivatives: modelling and pricing for agriculturals, metals and energy, Wiley, 2005.

Ects : 3

Lecturer :
HAFID AGOUZOUL

Total hours : 24

Overview :

The course is intended to be both theoretical and practical; its purpose is to introduce issues and problems that arise regarding pricing and hedging of exotic rate products. Specific examples of pricing and hedging will be dealt with. Concepts of DELTA and GAMMA/VEGA HEDGING will also be studied during the course. Recent advances in interest rate modelling will be introduced.

Course outline :

• Introduction of interest rate markets
• Delta Hedging products (Interest rate futures and bonds, Interest rate swaps, Zero coupon rate construction)
• GAMMA/VEGA Hedging products (Caps -Floors, Swaptions, Volatility Surface/Cube)
• Black and Scholes model -Numeraire Change- (Caps/floors, and swaptions pricing and cases Studies)
• SABR Model (Caps-Floor, and Swaption Smile, CMS pricing and convexity adjustment case study)
• Introduction to exotic interest rate derivatives (Bermudan swaptions, Callable bond and swap, Callable reverse floater, Callable snowball and Ratchet, Target Redemption Notes -TARNs, Range Accruals)
• HJM framework -HW 1 and 2 Factors (Cap- Floor and swaption pricing in the Hull White model -Hedge Basket, Calibration concepts)
• Introduction to multifactor models (Libor Market Model)
• Cheyette Model (Stochastic volatility)
• Case studies (Forward start options, Snowballs/Ratchets)
• VaR methodology in interest rates

Coefficient : 1.5

Learning outcomes :

Master the theory and practice of Fi Income products pricing and hedging.

Assessment :

Final exam

Bibliography-recommended reading

Brigo D. and F. Mercurio, Interest Rate Models-Theory and Practice With Smile, Inflation and Credit, Springer-Verlag Berlin and Heidelberg GmbH& Co. K; Édition : 2nd Revised edition 2005, 1037 pages.

Ects : 3

Lecturer :
THIBAUD VIENNE

Total hours : 24

Overview :

The objective of the course is to provide students with an introduction to supervised machine learning and its applications to finance. At the end of the course, students will be able to implement a whole machine learning pipeline in Python. From key features (data cleaning, cross-validation..) to machine learning models implementation (linear regression, tree-based techniques, neural networks...). Live-coding and practicing also are main features of the course. Students will be asked for multiple hours labs and a machine learning competition evaluation.

Course outline: Session 1: Machine learning in finance. Session 2: Linear and Logistic regressions. Session 3 : Machine learning in practice.

Labclass 1: Financial news impact on Dow Jones index. Session 4: Tree-based methods. Session 5: Feedforward neural networks.

Labclass 2: Machine learning competition.

Coefficient : 1.5

Require prerequisites :

Students must be enrolled in courses Python Programming and Applied Times series.

Learning outcomes :

Be able to implement a whole machine learning pipeline in Python. From key features (data cleaning, cross-validation..) to machine learning models implementation (linear regression, tree-based techniques, neural networks...).

Assessment :

Machine learning competition (50%), final evaluation (50%).

Bibliography-recommended reading

Trevor Hastie, Robert Tibshirani, Jérôme Friedman (2009), The elements of statistical learning, (Springer). Tuffery S. (2011), Data mining and statistics for decision making, (Wiley). Hinton Geoffrey (2014), Neural networks for machine learning, Toronto University. Ng Andrew (2014), Machine Learning, Stanford University.

Ects : 3

Lecturer :
THIERRY KUAGBENU
THIERRY BECHU
GUILLAUME MONARCHA

Total hours : 24

Overview :

We propose a deep dive into the factor investing universe, from its academic foundations to practical implementation. This course will consider two complementary perspectives, focusing on both:

• the structuring side of smart beta, factor-based, and alternative risk premia strategies, through the presentation of their implementation process, from stock selection to portfolio construction and strategy management
• a buy-side perspective, from performance attribution and strategy selection, to the management of multi-factor / multi-asset diversified portfolios.

The course is organized around four parts. First, we will introduce the academic foundations of factor investing, and present the typology of the current investment universe (smart beta, factor investing and alternative risk premia). The second part is dedicated to the presentation of long-only equity-based investment strategies (both smart beta and factor-based), and to the introduction of multi-factor investing. In the third one, we will review the alternative risk premia (ARP) universe across the various asset classes (equities, commodities, interest rates, FX), and we will address the issue of the management of ARP allocations. In the fourth part, we will consider the role of factor-based investment strategies (smart beta, factor investing and ARPs) within a diversified, multi-asset solution context.

Course outline:

Introduction (Guillaume Monarcha, 3h)

Part 1 (Thierry Béchu, 7h30)

• 1.1. Beta
• 1.2. Smart beta strategies
• 1.3. Factor investing
• 1.4. Multi-factor investing

Part 2 (Guillaume Monarcha, 9h)

• 2.1. Alternative risk premia
• 2.2. The management of ARP allocations

Part 3: Multi-asset solutions (Thierry Kuagbenu, 4h30)

• 3.1. Introduction
• 3.2. Theory
• 3.3. Practical applications

Coefficient : 1.5

Require prerequisites :

Be familiar with basic quantitative tools (statistics, estimation of linear models, constrained linear optimization), and portfolio theory.

Learning outcomes :

Master the technics, tools and strategies for alpha extraction

Assessment :

Exam and group project Group project: will consist in the construction and backtesting of systematic investment strategies, risk premia allocations, replication and application of research papers...

Ects : 3

Lecturer :
FABIAN ASTIC
MARIUS FRUNZA

Total hours : 21

Overview :

The aim of this course is to propose an out-of the box perspective upon the financial markets and to explore the financial universe beyond the traditional investments like equity, bonds, currency… . We will focus the course on the products and technics used at the fringe of finance including crowfunding, peer-2-peer finance, shadow banking, Bitcoin, social and environmental impact products….

Throughout this course students will learn about alternative investment supports and alternative financing solutions. The objectives of this lecture are:

• To understand the mechanism of alternative risks: global warming, catastrophic events including on the economy, …
• To explore new area including Environmental, Social, and Governance (ESG) Investment, cryptocurrency etc..
• To get familiar with modelling methods specific to alternative finance.

Course outline:

1. Alternative finance 101 Two faces of the same coin: as investors or as issuers. 2. Modelling methods for alternative finance

• Introduction to the non-Gaussian universe
• Real Option Theory
• Extreme value theory

3. Crypto-currencies: an alternative financial universe 4. Environmental, Social, and Governance (ESG) Investment 5. Crypto-currency : an alternative financial universe. 6. Alternative capital markets and Fintechs: Focus on Crowdfunding and P2P finance 7. Alternative Risk Transfer

• Climate risks
• Insurance and re-insurance. Focus on CAT Bonds

8. Fintech workshop (industry view)

Coefficient : 1.5

Require prerequisites :

Students must be enrolled in courses Applied Time series and must have past Introduction to Financial Econometrics, Financial Derivatives.

Learning outcomes :

Knowledge on the modelling methods specific to alternative finance.

Assessment :

Project

Bibliography-recommended reading

• Alexandridis, A. K. and A. D. Zapranis, 2013: Weather Derivatives, Springer, 300 pages.
• Barrieu P., and L. Albertini, 2009: The Handbook of Insurance-Linked Securities, Wiley, 398 pages.
• Frunza, M., 2010: Carbon allowances: A new financial asset, Editions universitaires europeennes, 164 pages.
• Guthrie G., Real Options in Theory and Practice, 2009, OUP, 432 pages.  

Ects : 3

Lecturer :
ALBERTO MANCONI

Total hours : 21

Overview :

Introduce students to this relatively new sub-discipline of finance which incorporates insights from cognitive and social psychology into finance. In the past 20 years behavioral finance has emerged as an important stream of thinking in finance. Relaxing the traditional assumptions of finance models has proved a fruitful way of understanding financial decision-making.

Course outline: The course will go through:

• The cognitive biases evidenced by cognitive psychologists;
• Financial anomalies and their interpretations through a behavioral finance lens;
• The implications of behavioral finance for investors and corporate financial policy.

Coefficient : Coefficient 1 (M2 Research in Finance) and Coefficient 1.5 (M2 Financial Markets)

Learning outcomes :

Relaxing the traditional assumptions of finance models has proved a fruit ful way of understanding financial decision-making and anomalies found in empirical tests.

Assessment :

Students will present a state-of-the art research paper among a selection of papers chosen by the instructors.

Bibliography-recommended reading

Daniel Kahneman, Paul Slovic, and Amos Tversky (eds.), Judgment under uncertainty: Heuristics and biases, Cambridge: Cambridge University Press, 1982. Richard Thaler, ed., Advances in behavioral finance, New York: Russell Sage Foundation, 1993. Richard Thaler, ed., Advances in behavioral finance, Volume II, New York: Russell Sage Foundation, 2005. Shleifer, Inefficient markets : an introduction to behavioral finance, Oxford, Oxford University Press 2000.

Ects : 3

Lecturer :
ARTHUR BAGOURD
GAELLE LE FOL
JIANG PU

Total hours : 21

Overview :

This course is a presentation of financial markets, trading mechanisms and their evolution dedicated to advancing the understanding and practice of electronic markets. A particular attention will be dedicated to optimal trading and execution technics but also on the use of algo trading startegies by market participants (who do what).

Coefficient : 1.5

Recommended prerequisites :

Derivative Pricing and Stochastic Calculus 2, Computational Finance, Advanced time series, Machine learning,

Learning outcomes :

This course is a presentation of financial markets, trading mechanisms and their evolution dedicated to advancing the understanding and practice of electronic markets. A particular attention will be dedicated to optimal trading and execution technics but also on the use of algo trading startegies by market participants (who do what).

Bibliography-recommended reading

• Bacidore, J. R., 2020, Algorithmic Trading Method: A practitioner's guide, TBG Press New York, 229 pages.
• Chan E., Algorithmic Trading- Winning Strategies and Their Rationale, Wiley, 2013, 207 pages.
• Guéant O., 2016, The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making, Chapman and Hall, 302 pages.
• Kissell, R., 2020 Algorithmic Trading Method: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques, Academic Press Inc, 2nd Edition, 612 pages.
• Lehalle C. A. and S. Laruelle, 2018, Market Microstructure in Pratice, World Scientific, 2nd Edition, 339 pages.
• Johnson B, 2010, Algorithmic Trading & DMA, Myeloma Press, 574 pages.

Ects : 3

Lecturer :
REDOUANE ZAD

Total hours : 18

Overview :

Structured Products offer tailor made investment solutions which can combine equities, currencies, commodities, credit or interest rates to meet specific investor’s needs in term of expected returns, frequency of cash flows and investment horizons and respect constraints such as risk level and specific legal and fiscal aspects. The most known Structured Products (Linked Notes) are based on exotic optional component to achieve their investment objectives. The last financial crisis leads to important changes in rules and regulations with regard to investor protection (MiFID II). A first effect was the simplification of Structured Products destined to retail market. Today Sophisticated products concern only the most discerning customers (institutional and client of private banks) The course will recall some key elements in fixed income and derivatives to concentrate first on exotic payoffs and then structured products. We will have a review of products from each main asset class. We will learn how to build them, how to hedge them, how to manage the life cycle and how to insure liquidity and create a secondary market. At the end of the course we will look to regulatory aspects, the cost of capital and liquidity and distribution rules. The course includes the uses of online pricer and simulations.

Course outline:

• Quick review of basics: Asset Class, derivatives, Greeks
• Exotic Options: Dispersion, Barrier, Digitals, Lookback, Autocallable
• Structured Products: introduction to the market, strategies, Linked Notes
• Structured Products Life cycle process: creation, pricing, secondary market
• Regulatory aspects: distribution rules, capital cost, liquidity
• How to improve valuation of derivatives using machine learning methods and their capacity to extract features and detect patterns from a large data set

Coefficient : 1.5

Learning outcomes :

Knowledge in structured products: how to build them (funded, or unfunded), how to hedge them and how to manage the life cycle.

Assessment :

Final Exam  

Bibliography-recommended reading

• Blümke, A., How to Invest in Structured Products: A Guide for Investors and Asset Manager, Wiley, 1st Edition, 392 pages, 2009.
• Bouzoubaa, M. and A. Osseiran, Exotic Options and Hybrids: A Guide to Structuring, Pricing and Trading, Wiley, 393 pages, 2016.
• Hull, John C., Options, Futures & other Derivatives, International Edition, 10th Edition, 2018.

Ects : 3

Lecturer :
TETE KAIZA AMOUH

Total hours : 21

Overview :

The course bears on the modeling and numerical analysis of financial derivatives. The objectives are:

• Understanding the financial meaning of the related mathematics: model parameters, implied volatility, Greeks.
• Learning how to derive a pricing equation based on the probabilistic formulation of a model, possibly with stochastic volatility and/or jumps,
• Learning how to implement a theta-scheme of finite differences or a tree pricing method,
• Learning simulation Monte Carlo pricing and Greeking methods: basic principles and variance reduction techniques, first in a set-up of random variables or vectors, then in a dynamic set-up of stochastic processes.

Course outline: 1) Motivating examples: Black-Scholes and Dupire model, Realized volatility vs Implied volatility vs Local volatility, 2) Derivation of the Pricing Equations in various models, 3) Deterministic Pricing Schemes: Finite Differences methods and Tree Methods 4) Simulation Pricing Schemes: simulation of random variables and stochastic processes, Pseudo Monte Carlo versus Quasi Monte Carlo, variance reduction techniques

Coefficient : 1.5

Require prerequisites :

Students must be enrolled in courses Derivative Pricing and Stochastic Calculus 2 and C++ Programming and must have past Financial Derivatives and Derivative Pricing and Stochastic Calculus 1

Learning outcomes :

Master the modelling and numerical analysis of financial derivatives

Assessment :

Project (in teams of two to three students)  

Bibliography-recommended reading

Crépey S., Computational Finance Lecture Notes, 2009 edition, 188 pages, available on www.maths.univ-evry.fr/crepey Lamberton D. and Lapeyre P., Introduction to Stochastic Calculus Applied to Finance. Chapman & Hall, 2nd revised edition, 2007. Shreve S., Stochastic Calculus for Finance II, Springer Finance, 2008. Hull J., Options, Futures, and Other Derivative Securities, Prentice-Hall, 7th edition, 2009.

Ects : 3

Lecturer :
ASHWIN JOSHI
KENZA AKALLAL
AYMERIC KALIFE
OLIVIER TOUTAIN

Total hours : 21

Overview :

The past years have seen a marked shift in society’s attitudes toward sustainability. This shift is spurring political pressure, a regulatory push and technological advancements to create the foundations of a more sustainable world, leading to a change in investor behaviour and setting in motion a major yet gradual capital reallocation. Society’s long transition toward the practice of sustainable investing is likely to drive market adjustments for years and even decades. In this course, we have a curated a series which will enable you to learn the basics and get started in the sustainable investing landscape, while providing you an opportunity to discover insights, data and tools across asset classes that are evolving the markets.

Course outline: Part 1: What is Sustainable Investing and why does it matter? --- 3h 1.1. Big picture - why sustainable development matters? 1.2. Evolving regulatory landscape 1.3. Recent market trends and strategy 1.4. Measuring sustainability 1.5. Limitations and challenge

Part 2: Sustainability’s challenge to corporates --- 3h 2.1. Externalities 2.2. Governance and behaviour 2.3. Strategy and intangibles - changing business models 2.4. Integrated reporting - metrics and data

Part 3: Approaches of data analysis --- 6h 3.1. ESG metrics methodology 3.2. Data availability, data quality and usage 3.3. Identification of material information 3.4. New tools and technology

Part 4: Financing Sustainability --- 6h 4.1. Investing for long-term value creation 4.2. Equity - Engagement and Stewardship, ESG integration in valuation 4.3. Bonds - investing without voting power 4.4. Alternatives - approach to sustainable investing in real assets 4.5. Banking - new forms of lending 4.6. Insurance - managing long-term risk 4.7. Market view - risk/return assessment

Part 5: How to get there? --- 3h 5.1. Current state of the market and financial institutions commitments 5.2. Next steps 5.3. Conclusion

Coefficient : 1.5

Require prerequisites :

A first course in Asset management

Learning outcomes :

In this course, we have a curated a series which will enable you to learn the basics and get started in the sustainable investing landscape, while providing you an opportunity to discover insights, data and tools across asset classes that are evolving the markets.

Bibliography-recommended reading

Schoenmaker D. and W. Schramade, Principles of Sustainable Finance, Oxford University Press, 432 pages, 2018.

Ects : 6