5.00 credits
22.5 h + 15.0 h
Q2
Teacher(s)
Coeurderoy Régis; Iania Leonardo;
Language
English
Prerequisites
The material covered in the courses of bachelor in Business Engineering. In particular, students are assumed to be familiar with basic concepts of statistics and econometrics, financial accounting, managerial accounting, and mathematics for business. Knowledge of statistical and econometrics programming languages such as Rstudio, and/or Matlab, etc, is assumed.
Main themes
We live in a complex environment, where the interconnections among economic agents (firms, consumers, ect.), their choices/decisions under uncertainty and as a response to unforeseen events determine the successfulness of firms’ activities. The last global economic crisis driven by the Covid19 pandemic, the great financial crisis, the digital transformation, and the pressing need for a transition towards a greener economy, are just some examples how complex and uncertain the firms’ competitive arena can be. In this course, students will learn basic tools that companies can use to identify, report and analyze the risks/opportunities that a complex environment can bring to firms’ activities.
Learning outcomes
At the end of this learning unit, the student is able to :  
Upon completion of this course, students will:


Content
1 What is risk management?
1.1 Introduction
1.2 Identifying and documenting risk
1.3 Fallacies and traps in risk management
1.4 Why safety is different
1.5 The Basel framework
1.6 Hold or hedge?
1.7 Learning from a disaster 13
2 The structure of risk
2.1 Introduction to probability and risk
2.2 The structure of risk
2.3 Portfolios and diversification
2.4 The impact of correlation
2.5 Using copulas to model multivariate distributions 49
3 Measuring risk
3.1 How can we measure risk?
3.2 Value at risk
3.3 Combining and comparing risks
3.4 VaR in practice
3.5 Criticisms of VaR
3.6 Beyond value at risk 82
4 Understanding the tails
4.1 Heavytailed distributions
4.2 Limiting distributions for the maximum
4.3 Excess distributions
4.4 Estimation using extreme value theory 115
5 Making decisions under uncertainty
5.1 Decisions, states and outcomes
5.2 Expected Utility Theory
5.3 Stochastic dominance and risk profiles
5.4 Risk decisions for managers 156
6 Understanding risk behavior
6.1 Why decision theory fails
6.2 Prospect Theory
6.3 Cumulative Prospect Theory
6.4 Decisions with ambiguity
6.5 How managers treat risk
7 Stochastic optimization
7.1 Introduction to stochastic optimization
7.2 Choosing scenarios
7.3 Multistage stochastic optimization
7.4 Value at risk constraints 224
8 Robust optimization
8.1 True uncertainty: Beyond probabilities
8.2 Avoiding disaster when there is uncertainty
8.3 Robust optimization and the minimax approach 250
9 Real options
9.1 Introduction to real options
9.2 Calculating values with real options
9.3 Combining real options and net present value
9.4 The connection with financial options
9.5 Using Monte Carlo simulation to value real options
9.6 Some potential problems with the use of real options 285
10 Credit risk 291
10.1 Introduction to credit risk
10.2 Using credit scores for credit risk
10.3 Consumer credit
10.4 Logistic regression
1.1 Introduction
1.2 Identifying and documenting risk
1.3 Fallacies and traps in risk management
1.4 Why safety is different
1.5 The Basel framework
1.6 Hold or hedge?
1.7 Learning from a disaster 13
2 The structure of risk
2.1 Introduction to probability and risk
2.2 The structure of risk
2.3 Portfolios and diversification
2.4 The impact of correlation
2.5 Using copulas to model multivariate distributions 49
3 Measuring risk
3.1 How can we measure risk?
3.2 Value at risk
3.3 Combining and comparing risks
3.4 VaR in practice
3.5 Criticisms of VaR
3.6 Beyond value at risk 82
4 Understanding the tails
4.1 Heavytailed distributions
4.2 Limiting distributions for the maximum
4.3 Excess distributions
4.4 Estimation using extreme value theory 115
5 Making decisions under uncertainty
5.1 Decisions, states and outcomes
5.2 Expected Utility Theory
5.3 Stochastic dominance and risk profiles
5.4 Risk decisions for managers 156
6 Understanding risk behavior
6.1 Why decision theory fails
6.2 Prospect Theory
6.3 Cumulative Prospect Theory
6.4 Decisions with ambiguity
6.5 How managers treat risk
7 Stochastic optimization
7.1 Introduction to stochastic optimization
7.2 Choosing scenarios
7.3 Multistage stochastic optimization
7.4 Value at risk constraints 224
8 Robust optimization
8.1 True uncertainty: Beyond probabilities
8.2 Avoiding disaster when there is uncertainty
8.3 Robust optimization and the minimax approach 250
9 Real options
9.1 Introduction to real options
9.2 Calculating values with real options
9.3 Combining real options and net present value
9.4 The connection with financial options
9.5 Using Monte Carlo simulation to value real options
9.6 Some potential problems with the use of real options 285
10 Credit risk 291
10.1 Introduction to credit risk
10.2 Using credit scores for credit risk
10.3 Consumer credit
10.4 Logistic regression
Teaching methods
The course will be centered around the following teaching methods:
 Inclass lectures
 Practical sessions
 Regular meetings with the professors and assistants
 Case studies
 Guest lecture
Evaluation methods
The evaluation methods are based on “Continuous Evaluation”, i.e. no exam is foreseen at the end of the teaching session. Students will work in groups on (i) exercises and (ii) concrete, reallife case studies, for which they will deliver a written report and an oral presentation. Individual evaluation will also be part of the final grade.
Bibliography
Teaching materials
 Book: Business Risk Management: Models and Analysis by Edward J. Anderson
 Slides, available on Moodle
Faculty or entity
CLSM
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] : Business Engineering