Master in Actuarial and Financial Science (Plan: 168 - Estudio: 224)
EPE
Coordinating teacher: MEILAN VILA, ANDREA
Department assigned to the subject: Statistics Department
Type: Compulsory
ECTS Credits: 6.0 ECTS
Course: 1º
Semester: 2º
Requirements (Subjects that are assumed to be known)
Actuarial Statistics
Fundamentals of Statistics, Linear Algebra, and Mathematical Analysis
Some familiarity with programming techniques based on MATLAB, R and Phyton is also recommended
Objectives
Knowledge of Multivariate Data Analysis with applications in Finance and Actuarial Sciences
Knowledge of Statistical software for Multivariate Data Analysis, with emphasis Phyton.
1. Population and sampling concepts
1.1 Random vectors and matrices
1.2 Expected values
1.3 Sampling distributions
2. Multivariate normal distribution
2.1 Basic properties
2.2 Simulation methods
2.3 Examples of application in finance
3. Multivariate Data
3.1 Multivariate data
3.2 The data matrix
3.3 Mean vector. Covariance and correlation matrices
3.4 Graphical methods
3.5 Linear combinations
4. Regression analysis
4.1 Simple and multiple regression
4.2 Diagnostic and residual analysis
4.3 Examples of application
5. Generalized linear models
5.1 Logit and probit models
5.2 Poisson regression
5.3 Multiple choice models
5.4 Examples of application
6. Principal components
6.1 Motivation and construction
6.2 Standardized case
6.3 Data example
7. Factor analysis
7.1 The orthogonal factor model
7.2 Estimation and rotation of factors
7.3 Examples of application
8. Cluster analysis
8.1 Distances and similarities
8.2 Hierarchical procedures: Ward's method
8.3 Nonhierarchical procedures: K-means method
8.4 Examples of application with real data
Learning activities and methodology
Competences will be acquired by students from:
1.Theory classes: one per week (14 sessions)
2. Practical classes in the computer room: one per week (14 sessions)
Activities 1. and 2. will be devoted to exercises, problems, data examples, and case studies. Teaching will make intensive use of the resources available in Aula Global.
Assessment System
% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50
JOHNSON, R. A. and WICHERN, D. W. . Applied Multivariate Statistical Analysis, Sixth Edition. Prentice Hall . 2007
KABACOFF, R. L. . R in action: Data analysis and graphics with R, Second Edition. Manning Publications. 2015
Additional Bibliography
EVERITT, B. and HOTHORN, T. . An Introduction to Applied Multivariate Analysis with R. Springer Verlag. 2011
FREES, E. W. . Regression Modeling with Actuarial and Financial Applications. Cambridge University Press . 2010
JAMES, G., WITTEN, D., HASTIE, T. and TIBSHIRANI, R. . An Introduction to Statistical Learning with Applications in R . Springer Verlag. 2013
MATLOFF, N.. The Art of R programming: A Tour of Statistical Software Design. No Starch Press. 2011
McNEIL, A., FREY, R. and EMBRECHTS, P. . Quantitative Risk Management: Concepts, Techniques and Tools, Revised edition. Princeton Series in Finance. 2015
ZUMEL, N. and MOUNT. J.. Practical Data Science with R. Manning Publications. 2014
The course syllabus may change due academic events or other reasons.
