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. 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:



  • 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



  • 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



  • 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.

In case of a pandemic like Corona the date, time and type of exam as well as use of aids may be changed. Any further information will be announced here in the Exam section.

  • 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

2 hours lectures every week and 2x2 hours every second week from week 6 to 20 (except holidays).

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
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-F21; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”

Please be aware:
- That it is the students´s own responsibility to continuously update themselves about their studies, their teaching, their schedule, their exams etc. through the study pages, the course description, the Digital Exam portal, Absalon, KUnet, myUCPH app, the curriculum etc.

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.

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. 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 must be complied and please be aware of the rules for co-written assignments.
• All parts must be answered in English and all parts must be uploaded to Digital Exam in one file.
All aids allowed

for the written exam.


Marking scale
7-point grading scale
Censorship form
No external censorship
for the written exam.
Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.


To receive the top grade, 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.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 42
  • Preparation
  • 116
  • Exam
  • 48
  • English
  • 206