# STATISTICAL MODELS AND METHODS FOR FINANCE-2

2023/2024
Official course title
STATISTICAL MODELS AND METHODS FOR FINANCE-1
Course code
EM1505 (AF:449600 AR:256271)
Modality
On campus classes
ECTS credits
6 out of 12 of STATISTICAL MODELS AND METHODS FOR FINANCE
Degree level
Master's Degree Programme (DM270)
Educational sector code
SECS-S/01
Period
1st Term
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
The course focuses on introducing univariate statistical techniques for estimating financial risk measures (e.g., volatility, value at risk, expected shortfall), when interest is on modeling prices and returns of financial assets.
Lectures will focus on providing a conceptual understanding of the unified nature of statistical inference in risk analysis, applying esplorative and estimation methods to analyze univariate phenomena in order to make data-based decisions.
Emphasis will be given to the correct and effective interpretation of results and to the development of critique data-based claims and decisions.
Particular attention will be devoted to the understanding of the proposed methods, both from a computational and a methodological perspective.
1. Comprehension:
- understanding the relationship between uncertainty and risk involved in financial activities
- understanding the main risk measures and their limitations
- understanding the most common probabilistic univariate models and their different characteristics
- understanding the inferential procedures based on the likelihood functions.

2. Applied knowledge:
- compute point and interval estimates of risk measures from univariate probabilistic model to prices and/or returns of a single asset
- use of explorative data-analysis tools to describe the empirical distribution of the observed data
- estimate an univariate statistical model via maximum likelihood
- selecting the best probabilistic model, among a set of candidates, using information criteria
- evaluating the uncertainty associated with the inferential conclusions

3. Evaluating:
- understand and describe with rigorous jargon the main aspects of data under investigation
- discuss the limits and benefits of the proposed statistical model in providing a representation of reality
- take decisions among competitive models, based on the empirical evidence
Basic knowledge of calculus, probability theory and statistics at undergraduate level
1. Risk, probability and risk measures
2. Recall of statistical inference (estimators, point and interval estimations, hypothesis testing)
3. Tools for exploratory analysis (histogram, quantile-quantile plot)
4. Univariate distributions and main properties (location-scale families, skewness, kurtosis)
5. Random vectors.
6. Introduction to estimation based on the likelihood function
Ruppert, D. (2011, 1st edition, or 2015, 2nd edition). 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), 19 (19.1-19.2), Appendix A.

or, equivalently,

Ruppert, D. and Matteson, D.S. (2015). 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), 19 (19.1-19.2), Appendix A.
The final assessment consists in a written exam including multiple choice questions and exercises. The exam is closed-notes and closed-book. Students are allowed to use a pocket calculator and two sides of an A4-sheet prepared by them at home.
Lecture-style instructions. Students are recommended to register to the course on Moodle platform, where they can find additional material (slides, exercises, software userguide and code, homework instructions) [https://moodle.unive.it/course/view.php?id=8096 ]
English
written
Definitive programme.
Last update of the programme: 21/12/2023