Master’s degree in Banking and Finance

Financial Risk Management

Course code

4S006189

Name of lecturer

Alessandro Gnoatto

Coordinator

Alessandro Gnoatto

Number of ECTS credits allocated

9

Academic sector

SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES

Language of instruction

Italian

Site

VERONA

Period

secondo semestre lauree magistrali dal Feb 25, 2019 al May 31, 2019.

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).

Syllabus

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)

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

Prerequisites:

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.

Assessment methods and criteria