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.
Education

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. PhD students must contact the study administration AND the lecturer in order to register for the research module and write the research assignment.
  • The course is a part of the admission requirements for the 5+3 PhD Programme. Please consult the 5+3 PhD admission requirements.
Learning outcome

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

 

Competencies:

  • 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.

Restrictions due to pandemic crisis:
The teaching in this course may be changed to be taught either fully or partly online due to a pandemic crisis like COVID-19. In case of changes and further information, please read the study messages in KUnet or the announcements in the course room on Absalon (for enrolled students).

  • 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” 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 two 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 overall schema can be seen at KUnet:
MSc in Economics => "courses and teaching" => "Planning and overview" => "Your timetable"
KA i Økonomi => "Kurser og undervisning" => "Planlægning og overblik" => "Dit skema"

Timetable and venue:
To see the time and location of lectures please press the link under "Timetable"/​"Se skema" at the right side of this page (F means Spring).

You can find the similar information in English at
https:/​/​skema.ku.dk/​ku2122/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-F22; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”

Please be aware:
- The study administration allocates the students to the exercise classes according to the principles stated in the KUnet.
- If too many students have wished a specific class, students will be registered randomly at another class.
- It is not possible to change class after the second registration period has expired.
- If there is not enough registered students or available teachers, the exercise classes may be jointed.
- The student is not allowed to participate in an exercise class not registered.
- The teacher of the exercise class cannot correct assignments from other students than the registered students in the exercise class except with group work across the classes.
- All exercise classes will be taught in English.
- The schedule of the lectures and the exercise classes can be changed without the participants´ acceptance. If this happens you can see the new schedule in your personal timetable at KUnet, in the app myUCPH and through the links in the right side and the link above.
- It is the students´s own responsibility continuously throughout the study to stay informed about their study, their teaching, their schedule, their exams etc. through the curriculum of the study programme, the study pages at KUnet, student messages, the course description, the Digital Exam portal, Absalon, the personal schema at KUnet and myUCPH app etc.

Written
Oral
Individual
Collective
Peer feedback (Students give each other feedback)

 

The students receive oral collective feedback during the content of the lectures.

Each student receives written individual peer feedback on the mandatory assignments.

ECTS
7,5 ECTS
Type of assessment
Portfolio, 48 hours
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.
____
Aid
All aids allowed

for the written exam.

Information about allowed aids for the re-examination, please go to the section "Re-exam".

__

Marking scale
7-point grading scale
Censorship form
No external censorship
for the written exam.
An oral re-examination may be with external assessment.
____
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