Applied Statistics to Finance

Base Knowledge

Probabilities.

Inferential statistics.

Teaching Methodologies

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

Learning Results

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.

Program

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

Curricular Unit Teachers

Maria Manuela Coelho Larguinho

Grading Methods

The evaluation of this subject is done through a practical work (mandatory) carried out in a group, with a maximum of 3 students, (NT), and a final exam, (NE).

The final grade (NF) is given by NF = 0.6*NE + 0.4*NT.

This evaluation is valid for all evaluation seasons (normal, appeal and special).

If students do not submit their work in the normal period, they will have to do so in one of the following evaluation periods.

The grade of the work delivered on the normal exam date  is valid for all evaluation periods.

For approval in the course  it is necessary to obtain a final mark of 10 or more.

The practical work will be evaluated and discussed throughout classes.


    Internship(s)

    NAO

    Bibliography

    Gujarati, D. (2015). Econometrics by Example, 2nd Edition, Palgrave Macmillan
    Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) using R. Springer Nature
    Ruppert, D., & Matteson, D. S. (2015). Statistics and Data Analysis for Financial Engineering, 2nd Edition. Springer
    Thakkar, J. J. (2020). Structural Equation Modelling. Application for Research and Practice. Springer
    Wooldridge, J.M. (2020). Introductory Econometrics: A Modern Approach, 7th Edition. Cengage Learning