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2023/2024  KAN-CCMVV1446U  Financial Econometrics

English Title
Financial Econometrics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course Second Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Rasmus Tangsgaard Varneskov - Department of Finance (FI)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 15-02-2023

Relevant links
Learning objectives
The course will provide students with an understanding of how carry out econometric analysis within different subfields of finance. In particular, the students will obtain a toolbox consisting of knowledge of modern models, methods, and econometrics that are required for analyzing financial data. Practical examples will be given. The main topics covered in the course are listed below:

- Return predictability, the efficient market hypothesis, nonlinear modeling
- Empirical market microstructure
- Portfolio choice and testing of the CAPM
- Multifactor Pricing Models
- Present Value Relations and Intertemporal Equilibrium Pricing
- Volatility estimation and modeling
- Continuous time processes
- Yield curve modeling
- Risk management and tail estimation

The students are required to know how apply this toolbox within these topics. In particular, they must:
  • demonstrate knowledge about how to model financial data
  • about how to estimate the parameters of said models
  • about how to carry out hypothesis testing
  • about how select the best performing models
  • about how to generate forecasts.
Course prerequisites
Baseline knowledge of statistics, econometrics and asset pricing.
Experience with coding is an advantage.
Examination
Financial Econometrics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Closed book: no aids
However, at all written sit-in exams the student has access to the basic IT application package (Microsoft Office (minus Excel), digital pen and paper, 7-zip file manager, Adobe Acrobat, Texlive, VLC player, Windows Media Player), and the student is allowed to bring simple writing and drawing utensils (non-digital). PLEASE NOTE: Students are not allowed to communicate with others during the exam.
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Course content, structure and pedagogical approach

This course deals with modern models, methods and econometrics for several subfields of finance including asset pricing, portfolio allocation and risk management.The students will learn the fundamental concepts of econometrics, testing, estimation, model selection, etc., and apply them to different financial datasets.

 

 
Description of the teaching methods
The course consists of lectures where the basic concepts are introduced and explained and problem sets where the students have the possibility to gain a deeper understanding of the concepts as well as practical knowledge of the methods presented in the lectures.
Feedback during the teaching period
Feedback will be given during the lectures as well as pre-arranged office hours. The feedback will be mainly concerned with the problem sets, where key concepts and methods will be reviewed and discussed. The lectures will look at a particular assignment and/or concept, whereas the students can visit during office hours to discuss the remaining assignments or additional material covered in the lectures.
Student workload
Student workload Preparation / exam 173 hours
Lectures 33 hours
Expected literature

Expected textbook:

 

Financial Econometrics: Models and Methods

Oliver Linton

Cambridge University Press

ISBN: 9781107177154

 

Complementary literature:

 

Welch, I. & Goyal, A. (2008), "A comprehensive look at the empirical performance of equity premium prediction", Review of Financial Studies 21, 1455-1508.

 

Andersen, T. G: & Varneskov, R. T. (2021), "Consistent Local Spectrum Inference for Return Regressions". NBER working paper 28569.

 

Farmer, L., Schmidt, L. & Timmermann, A. (2021) "Pockets of Predictability", Journal of Finance, forthcoming.

 

Diebold, F. X. & Strasser, G. (2013), "On the Correlation Structure of Microstucture Noise: A Financial Economics Approach", Review of Economic Studies, 80, 1304-1337.

 

Kyle, A. S. & Obizhaeva, A. A. (2016), "Market Microstucture Invariance: Empirical Hypotheses", Econometrica, 86, 1345-1404.

 

Kyle, A. S. & Obizhaeva, A. A. & Kritzman, M. (2016), "A Practitioner's Guide to Market Microstucture Invariance", Journal of Portfolio Management, 43, 43-53.

 

Neuhierl, A. & Varneskov, R. T. (2021), "Frequency Dependent Risk", Journal of Financial Economics, 140, 97-127.

 

Giglio, S., Kelly, B. & Xiu, D. (2021), "Factor Models, Machine Learning and Asset Pricing", Annual Review of Financial Economics, forthcoming.

 

Kelly, B., Pruitt, S. and Su, Y. (2019), "Characterstics are covariances: A Unified model of risk and return", Journal of Financial Economics, 134, 501-524.

 

Fama, E. & French, E. (2020), "Comparing Cross-sectional and Time-Series Models", Review of Financial Studies, 33, 1891-1926.

 

Novy-Marx, R. & Velikov, M. (2016), "A Taxonomy of Anomalies and their Trading Costs", Review of Financial Studies, 29, 104-147.

 

Phillips, P. C. B., Shi, S. & Yu, J. (2015), "Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500", International Economic Review, 56, 1043-1078.

 

Bansal, R. & Yaron, A. (2004), "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles", Journal of Finance, 59, 2004.

 

Constantinides, G. M. & Ghosh, A. (2011), "Asset Pricing Tests with Long-run Risks in Consumption Growth", The Review of Asset Pricing Studies, 1, 96-136.

 

Andersen, T. G., Bollerslev, T., Christoffersen, P. & Diebold, F. X. (2013), "Financial Risk Measurement for Financial Risk Managment", Handbook of Economics and Finance volume 2, Chapter 17, 1127-1220.

 

McAleer, M. & Medeiros, M. C. (2008), "Realized Volatility: A Review", Econometric Reviews, 27, 10-45.

 

Diebold, F. X. & Li, C. (2006), "Forecasting the term structure of government bond yields", Journal of Econometrics, 130, 337-364.

 

Gargano, A., Pettenuzzo, D. & Timmermann, A. (2019), "Bond return predictability: Economic value and links to the macroeconomy", Management Science, 65, 508-540.

 

Bianchi, D., Buchner, M. & Tamoni, A. (2020), "Bond Risk Premia with Machine Learning", Review of Financial Studies, 34, 1046-1086.
Last updated on 15-02-2023