STATISTICAL METHODS FOR RISK ANALYSIS

The course aims at introducing some statistical techniques for estimation of some financial risk measures (volatility, Value at risk and expected shortfall), when the problem is to model prices or returns of a single asset. Foundations of exploratory data analysis, probability and inferential statistics are developed, with emphasis on the computational aspects (with the GNU-R statistical software).

Knowledge and comprehension: understanding the relationship between uncertainty and risk involved in financial activities; understanding the most important probabilistic univariate models and their different characteristics; understanding the inferential procedures based on the maximum likelihood;

Applied knowledge and comprehension skills: to compute point and interval estimate of risk measures from univariate probabilistic model to prices and/or returns of a single asset; selecting the best probabilistic model, among a set of candidates, using information criteria; evaluating the uncertainty associated with the inferential conclusions

Basic knowledge of calculus, probability theory and statistics at undergraduate level. In particular, the students should be familiar with the contents of chapters 3-10 of Newbold et al. (2013) (see Further references under the textbook section).

1. Risk, probability and risk measures
2. Tools for exploratory analysis
3. Modeling univariate distributions
5. Risk Management

Ruppert, D. (2011). Statistics and Data Analysis for Financial Engineering, Springer, 2011, ch. 1, 2, 4, 5 (5.1-5.5, 5.7-5.10, 5.12-5.14), 7 (7.1-7.5), 19 (19.1-19.3), Appendix A.

R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
URL http://www.R-project.org/ .

Newbold, P.,Varlson, W. and Thorn, B. (2013): Statistics for Business and Economics. Pearson

The final assessment consists in a 60 minute written exam including multiple choice questions and exercises. A homework, consisting in the analysis of a dataset, can be submitted by students who achieve a mark greater than 25 in the written exam. Such homework can increase the written exam mark of at most 4 points.
The exam rules could be modified due to the COVID-19 emergency.

The professor will use interactive lecture-style presentations and students will be required to actively participate. Students are recommended to register to the course on Moodle platform (https://moodle.unive.it/view.php?id=214 ), where they can find additional material (slides, exercises, software userguide and code, homework instructions).

English

Students are invited to enrol to the course at the e-learning platform (https://moodle.unive.it/course/view.php?id=214 ).

written and oral