Financial Econometrics

Description

Introduction: Financial Econometrics is designed to help students understand and apply statistical techniques to financial data. Students will gain valuable analytical and programming skills that will enhance their ability to conduct research and perform financial analysis in industry settings. The course will center on linear regression, which is the most commonly used statistical technique in finance. It will provide a strong foundation for anyone interested in pursuing a quantitative role in the financial sector. Course content: 1. Review of the mathematical and statistical foundations of financial econometrics. 2. Overview of the collection, transformation, and interpretation of financial data. 3. The capital asset pricing model (CAPM), which will be used in econometric exercises throughout the course. 4. Linear regression and its extensions, which form the foundation of financial econometrics. 5. Diagnostic tests for problems in linear regression. Learning outcomes - Knowledge: During the course, students should develop knowledge about: - Regression analysis, which is frequently used to analyze financial data. - Statistical inference, which can be used to draw conclusions about populations using a sample of data. - Common issues and practices with financial data. Learning outcomes - Skills: Upon completion of the course, the students should be able to: - Estimate regression models. - Perform hypothesis tests on the parameter estimates of the regression model. - Test the assumptions underlying the classical linear regression model. - Extract data from databases (e.g WRDS) and implement econometric techniques in the R programming language. General Competence: In the course, the focus will be on the assumptions underlying the different theories and methods covered. Hence, it is expected that students have a critical attitude towards the realism of these. Teaching and learning activities: The course elements include lectures, in-class exercises, online exercises, a graded group assignment, and a final exam. During the lectures, we will introduce new econometric techniques and discuss their practical application in R. To strengthen the students' understanding of these concepts, they will have to submit a group assignment. This will involve downloading the data themselves from a database (e.g., WRDS), importing the data into R, implementing the econometric analyses in R, and summarizing the results in tables or graphs. Here, strong emphasis will be placed on the interpretations of the results from a statistical as well as from an economic point of view. Software tools: R Qualifications: Higher Education Entrance Qualification Disclaimer Deviations in teaching and exams may occur if external conditions or unforeseen events call for this. Required prerequisite knowledge: MET 2910 Mathematics and MET 2920 Statistics or equivalent. Assessments: Assessments Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Group (1 - 4) Duration: 1 Semester(s) Comment: Group Assignment. All exams must be passed to obtain a final grade in the course. Exam code: FIN 36182 Grading scale: ECTS Resit: Examination every semester Exam category: School Exam Form of assessment: Structured Test Exam/hand-in semester: First Semester Weight: 60 Grouping: Individual Support materials: - BI-approved exam calculator - Simple calculator - Bilingual dictionary Duration: 2 Hour(s) Comment: Final examination. All exams must be passed to obtain a final grade in the course. Exam code: FIN 36183 Grading scale: ECTS Resit: Examination every semester Type of assessment: Ordinary examination All exams must be passed to get a grade in this course. Total weight: 100 Student workload: Activity Duration Comment Teaching 30 Hour(s) Prepare for teaching 75 Hour(s) Group work / Assignments 30 Hour(s) Student's own work with learning resources 50 Hour(s) Digital resources 15 Hour(s) Student’s own work with learning platform material Sum workload: 200 Incoming exchange students at BI Norwegian Business School select courses from BI's official semester-specific exchange course lists. BI defines a full semester as 30 ECTS; bachelor courses are typically 7.5 ECTS each (4 per semester) and master courses are typically 6 ECTS each (5 per semester). Course availability, prerequisites, and timetabling are confirmed via BI's International Office and the Learning Agreement process.