Financial Risk Management (2020/2021)

Course code
Name of lecturer
Alessandro Gnoatto
Alessandro Gnoatto
Number of ECTS credits allocated
Academic sector
Language of instruction
secondo semestre (lauree magistrali) dal Mar 1, 2021 al Jun 1, 2021.

Lesson timetable

Go to lesson schedule

Learning outcomes

The goal of the lecture is to present the theoretical foundations and the models employed by financial institutions to manage different sources of financial risk. A particular focus will be put on numerical methods (Monte Carlo simulations) and their implementation using modern IT-Tools (Java, Eclipse).


Part 1: Monte Carlo Methods Basic notions: expectation, Lp spaces, classical inequalities (Markov, Chebychev etc...) Classical numerical integration Monte Carlo integration (code) Generation of random draws and discretization of stochastic processes (code) Variance reduction techniques (code)

Part 2: Market Risk Introduction: IR, Equity, FX, Commodities, Options Risk Measures: general theory VaR/ES calculation

  1. Historical approach (code)

  2. Analytical approach

  3. Monte Carlo simulations (code)

Optional: Basel II regulations

Part 3: Credit Risk Basic risks in a default-free setting: duration and convexity Structural Models Rating based models Reduced form models Optional: Basel II regulations

Part 4: Counterparty Credit Risk Funding and collateral (xVA) CVA DVA FVA Monte Carlo for xVA (code) Optional: Basel III/Basel IV regulations


  1. A good working knowledge of mathematical analysis (limits/derivatives/integration). The ability to solve standard first and second order equations/inequations.

  2. A good working knowledge of basic statistics (probability distributions, conditional probabilities, random variables, central limit theorem, law of large numbers, statistical tests, conditional/unconditional expected values/moments).

  3. Programming: the lecture does not assume that students are experienced Java programmers, anyway attendance of the block-lecture Introduction to Java Programming, offered before the lectures starts, is recommended. It is assumed that students are able to write simple programs in any language such as Matlab, Python, Visual Basic, Turbo Pascal etc. In summary, it is assumed that students are able to think in an algorithmic way, independently of any programming language. Practical tutorials for the Java programming language will be provided.

Reference books
Author Title Publisher Year ISBN Note
Baesens, B., Backiel, B. and Vanden Brouke, S. Beginning Java Programming: The Object-Oriented Approach (Edizione 1) Wrox Pr Inc 2015 978-1-118-73949-5
Bielecki, T. and Rutkowski, M. Credit Risk: Modeling, Valuation and Hedging (Edizione 2) Springer 2004 978-3-662-04821-4
A. F. McNeil, R. Frey, P. Embrechts Quantitative Risk Management:Concepts, Techniques and Tools Princeton University Press 2015

Assessment methods and criteria

The exam consists of two parts: the first is a Project Work that has to be completed by using the Java programming language. The mark on the project work has a weight of 30% on the final grade.

The Project Work can be completed by groups consisting of up to 4 people.

Aims of the project work are:

implement and deepen the understanding of the methods illustrated during the lecture.
improve the ability to work in teams.

The grade of the project work is valid for all written exams during the current academic year and for the first two examinations of the next academic year.

Students get access to the written exam only if the project work has a positive valuation. Those who do not submit any solution will not be accepted to the exam.

The second part of the exam consists of a written exam on all topics of the lecture. The exam contain theoretical and practical exercises together with programming questions related to the Java programming language. In case the grade is greater or equal to 18, the written exam has a weight of 70% on the final mark.