Learning outcomes

By the end of the course, students should be able to:

  • construct and interpret financial returns and descriptive statistics;

  • identify key empirical features of financial time series that are relevant for risk measurement;

  • understand and apply the main approaches to Value-at-Risk (VaR);

  • compare historical, parametric and Monte Carlo VaR and identify their assumptions, strengths and limitations;

  • critically interpret financial risk measures;

  • implement and interpret a VaR backtesting procedure;

  • relate quantitative risk measurement techniques to risk management practices in financial institutions.

Goals

The course aims to provide students with a solid understanding of the main concepts and tools used in financial risk management, with particular emphasis on market risk.

It seeks to establish the link between financial assets, market data, statistical risk measures and risk management decisions. The course also aims to develop students’ ability to critically assess the results produced by risk models and to understand the conditions under which these models can be used in practice.

Content

The course covers the following topics:

  • financial assets and the concept of market risk;

  • financial data, including frequency, sources, cleaning and adjustments;

  • simple returns, log returns and their relationship with P&L;

  • stylised facts of financial returns, including volatility clustering, fat tails and crisis correlations;

  • basic risk measures, including volatility, drawdown and quantiles;

  • Value-at-Risk and its statistical interpretation;

  • historical or non-parametric VaR;

  • parametric VaR;

  • Monte Carlo VaR;

  • comparison of alternative VaR methodologies;

  • backtesting and model validation;

  • practical perspectives on financial risk management through sessions with practitioners from the banking sector.

The course progresses from financial assets and data to risk measurement, model validation and practical applications.

Table of contents

  1. Introduction to financial risk management

  2. Financial assets, data and returns

  3. Statistical properties of financial returns

  4. Basic risk measures

  5. Introduction to Value-at-Risk

  6. Historical VaR

  7. Parametric VaR

  8. Monte Carlo VaR

  9. Comparison of VaR methodologies

  10. Backtesting and model validation

  11. Limitations and critical assessment of risk measures

  12. Financial risk management in practice

Exercices

Students apply the concepts covered in the course to real or simulated financial data.

Exercises include:

  • calculation and interpretation of financial returns;

  • descriptive analysis of financial time series;

  • calculation and comparison of alternative risk measures;

  • implementation of historical, parametric and Monte Carlo VaR;

  • interpretation of differences across methodologies;

  • backtesting of a VaR model;

  • critical analysis of model outputs.

A project allows students to integrate and apply the main concepts and techniques covered throughout the course.

Teaching methods

Teaching methods include:

  • lectures on the main concepts and methodologies;

  • analysis of financial data and empirical examples;

  • practical application of alternative VaR methodologies;

  • completion of a risk measurement project;

  • exercises in the interpretation and validation of risk measures;

  • discussion and Q&A sessions;

  • guest sessions with financial-sector practitioners, providing students with insights into the use of risk management tools in professional and institutional settings.

Teaching materials include lecture slides, video capsules and a dedicated syllabus on Value-at-Risk.

Assessment method

Assessment consists of two components:

1. VaR project — 50% of the final grade

Students complete a project focusing on the measurement and analysis of financial risk. The project also includes an oral presentation or defence.

2. Examination — 50% of the final grade

The examination includes:

  • questions on the concepts and methodologies covered in the course;

  • a practical assignment carried out in a computer room.

Assessment focuses in particular on the student’s ability to connect financial data, risk measurement, interpretation and critical assessment of the results.

Sources, references and any support material

The following materials are provided:

  • lecture slides;

  • video capsules;

  • a dedicated syllabus on Value-at-Risk;

  • project instructions and supporting data.

Indicative reference:
Philippe Jorion, Value at Risk: The New Benchmark for Managing Financial Risk.

Language of instruction

French
Training Study programme Block Credits Mandatory
Master in Management Standard 1 5 Yes
Master in Management Finalité didactique 1 5 Yes
Master in Management, Professional focus in Digital Enterprise Transformation Standard 1 5 No
Master in Management Finalité spécialisée 1 5 Yes
Master in Business Engineering Finalité spécialisée en data science 1 5 Yes
Master in Business Engineering Finalité spécialisée en Sustainable & Digital Management 1 5 Yes
Master in Business Engineering Finalité spécialisée en Sustainable & Digital Management 2 5 Yes
Master in Management Finalité didactique 2 5 Yes
Master in Management, Professional focus in Digital Enterprise Transformation Standard 2 5 No
Master in Management Finalité spécialisée 2 5 Yes
Master in Business Engineering Finalité spécialisée en data science 2 5 Yes