Applied Statistics to Finance

Classes are, according to what is determined in the curriculum, theoretical-practical, planned and prepared according to active
methodologies, to have an active involvement of all students at various times or in the entire class.
In the theoretical part, of introduction of concepts, fundamental results and methods, the expository method will tend to be used,
interspersed with tasks that elicit an active participation of all students. These tasks include asking questions to and by the students, orally,
and also proposing a debate/discussion in small groups on some aspect/topic exposed.
The practical part will be aimed at the full development of the skills listed, through the commented exemplification of procedures and/or
problem solving under the guidance/tutoring of the teacher, encouraging autonomous work or in small groups. A strong interaction between
theory and practice will prevail, a central role in the visualization and treatment of concrete and real situations

Statistics is a science that has a large number of applications in the most diverse areas of knowledge and professional segments, namely in
the area of Finance. Data-based decision-making depends on statistical knowledge that is used from planning to analysis and interpretation.
In this course, some inferential statistical methods for data analysis and statistical models applied to financial data will be addressed.
The following learning objectives are thus defined:
1. to know and apply the main statistical concepts to real financial data;
2. to know the definitions of returns and apply the main statistical concepts;
3. to identify and apply statistical models applied to the resolution of real problems;
4. to develop complex statistical studies;
5. to use software to support the implementation of statistical techniques.

1. Introduction
1.1. Descriptive Statistics
1.2. Statistical Inference
2. Asset Returns
2.1 Definitions and Properties
2.2. Normality Tests
3. Statistical Models Applied to Finance
3.1. Linear Regression Model
3.2. Exploratory Factor Analysis and Confirmatory Factor Analysis
3.3. Structural Equation Models
3.4. Models with Discrete Dependent Variable
3.5. Time Series Models

NAO