Analyses of Financial Data

Description

Introduction: This course covers important econometric techniques that are used in empirical finance. The main focus will be on time series econometrics as time series is the most frequent data format in finance. The course starts by examining basic regression analysis. Then it continues on to forecasting and the modelling of long-run relationships in finance, before it finally focuses on the modelling of volatility. Course content: This course introduces students to empirical techniques that are relevant for finance and business and economics in general. More specifically, the outline of the course is as follows: Foundations for empirical methods in finance. - What is econometrics? - Regression analysis with Ordinary Least Squares (OLS) - Introduction to R - Regression with Dummy variables - Univariate time series models and forecasting - Modelling long-run relationships in finance - Modelling volatility Learning outcomes - Knowledge: The aims of this course are to: - Introduce students to important empirical quantitative techniques used in finance and more generally in business and economics. - Make students able to apply them appropriately. - Prepare students for subsequent course work in finance, business, and economics. More specifically, on completion of the course the students acquired knowledge and skills should be as follows: - Understand in depth what is meant by correlation and regression analysis - and the difference between them - Understand in depth what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model - Understand in depth the limits and assumptions of regression analysis and the consequences that follows from violations of the different assumptions - Understand the rationale for dummy variables; what they are and how they can be used - Understand the rationale for univariate time series and how these can be for forecasting economic variables - Understand how long-run relationships in finance can be modelled. More specifically; understand the concepts of Stationarity, Cointegration and Error Correction Models - Understand how the volatility of economic variables can be modelled. More specifically; understand how ARCH-type of models work - Understand how the programming language R works and be familiar with basic R syntax Learning outcomes - Skills: On completion of the course students should be able to use software like R in order to: - Perform basic data handling - Estimate financial models formulated as linear regression models - Test the statistical assumptions underlying OLS - Estimate regression models with dummy variables - Estimate appropriate univariate time series models and use them to forecast economic variables, and then evaluate the quality of the forecast - Test for Stationarity and Cointegration - Estimate Error Correction Models - Estimate appropriate volatility models (ARCH-type models) General Competence: Students will acquire a conscious and critical attitude towards different data types and their handling, towards econometric analysis, and towards the assessment and interpretation of results from empirical research. Students will also by completing the course develop both their analytical and programming skills. Teaching and learning activities: Each topic will be accompanied by a hands-on practical application of an empirical finance topic. The software package R will be an integral part of the coursework. R is a software program that has become a standard for data analysis inside academia and corporations, especially in the finance industry. It is an open source software available free of charge on internet. The use of R will introduce students to some of the basics of programming. Programming is a skill typically required in the financial industry. If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or in the textbook. Computer-based tools: Google, Yahoo finance, Quandl, and itslearning. Software tools: R/R-Studio 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: EXC 2910 Mathematics, EXC 2904 Statistics and EXC 3506 Research Methods and Econometrics. Or equivalent. Assessments: Assessments Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 30 Grouping: Group/Individual (1 - 4) Duration: 2 Week(s) Comment: Written home assignment. All exams must be passed to obtain a final grade in the course. Exam code: EXC 36722 Grading scale: ECTS Resit: Examination every semester Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Support materials: - BI-approved exam calculator - Simple calculator - Bilingual dictionary Duration: 3 Hour(s) Comment: All exams must be passed to obtain a final grade in the course. Exam code: EXC 36723 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 39 Hour(s) Seminar groups 3 Hour(s) Computer sessions. Student's own work with learning resources 146 Hour(s) Review of the slides every evening after the lecture. Group work / Assignments 12 Hour(s) 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.