RISK MEASUREMENT

This is a course in financial econometrics with an emphasis on the concepts, techniques and tools required for quantitative risk management. The focus will be on the statistical modeling of financial time series (asset prices and returns) with an emphasis on univariate e multivariate models for conditional herteroskedasticity for quantitative risk management (GARCH), and the analysis of extreme values. Final goal is to define and validate risk measure statistiocs, commonly used for financial markets.

The learning goals/objectives of the course are: (1) survey the relevant theoretical and practical techniques for risk measurement; (2) introduce state-of-the-art techniques for modeling financial time series and managing financial risk with particular emphasis on GARCH models for conditional heteroskedasticity; (3) use of statistical/econometrics software to get hands-on experience with real world data.

Econometrics
Statistics (hypothesis testing)
Probability

Topics to be covered include:

- Risks in finance
- Empirical properties and stylized facts of asset returns
- Probability distributions and statistical models for asset returns
- Volatility and correlation modeling (GARCH models)
- Extreme Value Theory: block maxima and peaks-over-the-threshold
- Risk measures: definitions and estimates
- Backtesting
- Applications through RStudio

- Danielsson, J. (2011). Financial Risk Forecasting. Wiley Finance.
- Further material shared by the instructor (moodle)

Further readings:
- Tsay, R. (2010). Analysis of Financial Time Series, Third Edition. Wiley.

The exam will be written with theoretical questions and/or exercises, aiming at evaluating the ability to interpret output from real data problems

written

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

A. Grades in the 18–22 range will be awarded when the following criteria are met:

- sufficient knowledge and theoretical and applied understanding of the course content;
- limited ability to interpret empirical and/or theoretical results;
- sufficient communication skills, particularly with regard to the use of subject-specific terminology.

B. Grades in the 23–26 range will be awarded when the following criteria are met:

- satisfactory knowledge and theoretical and applied understanding of the course content;
- satisfactory ability to interpret empirical and/or theoretical results;
- satisfactory communication skills, particularly with regard to the use of subject-specific terminology.

C. Grades in the 27–30 with honours range will be awarded when the following criteria are met:

- good or excellent knowledge and theoretical and applied understanding of the course content;
- good or excellent ability to interpret empirical and/or theoretical results;
- fully appropriate communication skills, particularly with regard to the use of subject-specific terminology.

Lectures and practice sessions through RStudio.