Brief Course Description
1. Course Title:
Regression Models in Financial Markets
2. Language of Instruction:
Tiếng Việt
3. Course Code:
BAN506023
4. Credits:
3
5. Course Objectives:
Knowledge: Students who complete this course will have knowledge in the field of financial markets and apply it to specialized and specific economic models. Upon completion of the course, students will be able to combine knowledge from finance, statistics, mathematics and identify limitations in the field of quantitative analysis when processing actual data. The content focuses on traditional tools while staying up-to-date with the latest tools of quantitative analysis. With the knowledge of the subject, students can take on analytical and informational decision support positions for senior and middle managers. Graduates have a clear understanding of the limits of knowledge and the content of further study in the field. Skills: Students can work effectively in analytical teams and collaborate on academic research. Students have the ability to conduct in-depth research on their own in the field of regression modeling. Students have the ability to analyze and gather, including, categorize information, identify necessary information, know how to identify, evaluate at a depth level, and how to handle related problems. Students can fully contribute to their professional knowledge, approach and evaluation of strategic decisions.
6. Brief Description of Course Content:
This course is designed for undergraduate students majoring in Finance and Banking. Upon completion, students will be able to integrate theoretical knowledge acquired from previous courses, such as Financial Markets and Institutions, Commercial Banking, Risk Management, and Security Analysis and Valuation, with the practical applications covered in this course.
Additionally, the course equips students with the necessary knowledge and skills for conducting research-oriented graduation theses and provides a solid academic foundation for graduate studies.
The key topics covered in this course include:
- Econometrics and Regression Model Development
- Linear Regression Model (LRM) – Components, Assumptions, and Estimation (OLS, ML)
- Generalized Linear Regression Model (GLM) – Heteroscedasticity, Autocorrelation, Multicollinearity, and Hypothesis Testing
- One-Equation Econometric Models – Lagged Variables, Dummy Variables
- Multiple-Equation Models – Simultaneous Models and Structural Parameter Estimation
- Econometric Forecasting