Advanced Empirical Finance: Topics and Data Science (F)
Course content
This course considers challenges in the empirical analysis of a range of topics in advanced finance. The course relates to the finance course on “Corporate Finance and Incentives”, the volatility modeling course “Financial Econometrics A” and to the financial econometrics content of “Advanced Macro and Financial Econometrics”.
The course puts a great amount of emphasis on the practical implementation of financial econometric methods. In terms of data, which is core to empirical finance, we discuss in detail how to obtain and use historical stock price and company information as well as high-frequency intra-day data and other types of “vast data” sets in relation to risk analysis and risk management. This includes an introduction to data science applications such as data visualization and optimization techniques, as well as practical handling of large datasets.
Further, the course material provides tools for communicating the empirical findings such as the creation of interactive figures, ready-to-publish tables and figures, and writing well-documented codes. We carry out the empirical analyses in a programming language such as “R”.
In terms of empirical finance topics, the course covers advanced areas, including:
- Estimation and testing of asset pricing factor models as well as their applications in factor investing strategies. This includes a discussion of recent econometric methods for evaluating, testing and implementing recent asset pricing models. In addition, the course treats commonly used machine-learning methods, e.g. Lasso and neural networks, with emphasis on their application in relation to factor selection. A focus area is the analysis of “vast data” and an introduction to key tools such as importing, cleaning, and summarizing large amounts of stock market data obtained (for instance) from the CRSP database for instance to construct portfolio sorts to identify factor risks.
- Analysis of high-frequency intra-day transaction and orderbook data for volatility forecasting for asset allocation and risk management. This includes the estimation of multivariate realized volatility measures based on intra-day data, estimation of selected continuous-time models, as well as a discussion of the statistical properties of the applied econometric methods. Moreover, this part treats important aspects of handling big data sets of high-frequency data, as well as data issues arising from market microstructure noise contamination.
- Examples of additional areas, or topics, include: (i) Market frictions, transaction costs, liquidity risk and (ii) incomplete markets.
MSc programme
in Economics – elective course
The PhD Programme in Economics at the Department of Economics:
- The course is an elective course with research module. In order to register for the research module and to be able to write the research assignment, the PhD students must contact the study administration AND the lecturer.
- The course is a part of the admission requirements for the 5+3 PhD Programme. Please consult the 5+3 PhD admission requirements.
The course is open to:
- Exchange and Guest students from abroad
- Credit students from Danish Universities
- Open University students
After completing the course the student is expected to be able to:
Knowledge:
- Account for the core steps of data science applications (e.g. data wrangling, visualization, modelling, communication)
- Define and describe stylized facts of financial asset returns
- Discuss and criticize multifactor asset pricing models
- Identify and account for portfolio sorts
Skills:
- Master R scripts and writing functions
- Import, clean and analyze financial market data from different data sources
- Apply shrinkage methods such as Lasso to factor selection problems
- Evaluate the performance of estimated mean variance efficient portfolio weights
- Evaluate and implement the estimation of risk measures such as realized volatility based on high-frequency data
- Create efficient code to estimate multivariate realized volatility measures
- Debate and present the problems of microstructure-noise in high-frequency data
- Apply methods suitable for big-data problems such as parallel and cloud computing
- Create Shiny widgets and interactive reports with R
Competences:
- Plan, perform and implement data-science applications from scratch
- Master and carry through relevant asset pricing models and solutions in new, unpredictable and complex contexts.
Lectures and group-based assignements with peer-to-peer feedback.
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Hadley Wickham and Garrett Grolemund; 2017; O’Reilly Media Inc.; ISBN: 1491910399)
- Empirical Asset Pricing: The Cross Section of Stock Returns (Turan Bali, Robert Engle, Scott Murray; 2016; Wiley & Sons; ISBN: 9781118095041
- Recent research papers provided via Absalon
It is recommended to have followed the courses “Corporate
Finance and Incentives”, "Financial Decision Making"
and/or “Financial Econometrics A” before or at the same time as
"Advanced Empirical Finance: Topics and Data Science" but
it is not a prerequisite. If the students have not followed one of
the courses, the students may study harder in the beginning of the
course.
It is recommended that Econometrics II is followed at least at the
same time of "Advanced Empirical Finance: Topics and Data
Science
Schedule:
2 hours lectures every week and 2x2 hours every second week from
week 6 to 20.
2 hours of exercise classes every second week from 6 to
20.
The students receive oral collective feedback during the content of the lectures.
Each student receives written individual peer feedback on the mandatory assignments.
for enrolled students: Rules etc at Master(UK) and Master(DK)
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice- read more through this website.
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment, 48 hours
- Type of assessment details
- The exam is a written assignment consisting of two parts:
• Part 1: The first part is based on one of the mandatory assignments worked on during the course. The student can use the peer feedback received during the course to improve this assignment. This can be done before the exam period begins. The repeat assignment is chosen at random and reveals with the release of the exam.
• Part 2: The second part is a new assignment given in English
Please be aware that:
• The assignments can be written individually or by groups of maximum three students.
• The plagiarism rules and the rules for co-written assignments must be complied.
• All parts must be answered in English
• All parts must be uploaded to Digital Exam in one file. - Examination prerequisites
-
To qualify for the exam the student must no later than the given deadlines during the course:
- Hand in a minimum of 2 out of 3 mandatory assignments.
- Provide useful written peer feedback based on specific criteria for a minimum of 2 out of the 3 mandatory assignments to two students from other groups.
Please be aware of:
- The lecturer controls the assignments and the feedback.
- The assignments can be written individually or by groups of maximum three students. The peer feedback must be written individually.
- The plagiarism rules must be complied and please be aware of the rules for co-written assignments.
- The assignments and the peer feedback must be written in English.
- The mandatory assignments and the peer feedback are part of a portfolio exam. See “Type of assessment”
- Aid
- All aids allowed
Use of AI tools is permitted. You must explain how you have used the tools. When text is solely or mainly generated by an AI tool, the tool used must be quoted as a source.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
for the written exam. The oral re-examination may be with external assessment.
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
More information is available in Digital Exam from the middle of the semester.
More information about examination, rules, aids etc. at Master (UK) and Master (DK).
- Re-exam
-
Individual 20 minutes oral examination without preparation time
To qualify for the exam the student must no later than the given deadlines during the course:
• Hand in a minimum of 2 out of 3 mandatory assignments.
• Provide useful written peer feedback based on specific criteria for a minimum of 2 out of the 3 mandatory assignments to two students from other groups.Reexam information:
The reexamination date/period can be found in the reexam schedule here
More information in Digital Exam in August.
More information at Master (UK) and Master (DK).
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
In order to obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
In order to obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
Single subject courses (day)
- Category
- Hours
- Lectures
- 42
- Preparation
- 116
- Exam
- 48
- English
- 206
Kursusinformation
- Language
- English
- Course number
- AØKK08407U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
Ph.D.
- Duration
-
1 semester
- Placement
- Spring
- Price
-
Information about admission and tuition fee: Master and Exchange Programme, credit students and guest students (Open University)
- Studyboard
- Department of Economics, Study Council
Contracting department
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Stefan Voigt (12-7a7b6c6d6875357d76706e7b476c6a767535727c356b72)
Teacher
See "Course Coordinators"
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Kursusinformation for indskrevne studerende