FIN620 Financial Econometrics

Course Catalog Description


This course will cover the main topics of the analysis of time series to evaluate risk and return of the main products of capital markets. Students will work with historical databases, conduct their analysis, and conduct tests based on the techniques reviewed during the class.

Prerequisites: BIA 652 Multivariate data analytics or MGT 700 Econometrics

The significant amount of historical information available for most financial instruments requires a systematic and analytical approach to select an optimal portfolio. Time series analysis facilitates this process understanding, modeling, and forecasting the behavior of financial assets.

This course reviews the most important techniques used by investors, risk managers, and also by finance managers of non-financial service companies to analyze time series of their most relevant financial variables. Even though the methodologies reviewed during this course could also be applied to other domains such as marketing, the main emphasis of this class is on financial applications with special consideration to risk management.

Relationship of Course to Rest of Curriculum

Students will have the opportunity to formalize the concepts of quantitative finance in econometric models that can be applied to risk management or trading.

Campus Fall Spring Summer
On Campus
Web Campus


Professor Email Office
German Creamer Babbio 637

More Information

Course Outcomes

By the end of this course, the students will be able to:

  1. Understand the foundations of financial time series data, including high-frequency data
  2. Apply models and methods for analysis of financial time series (return and volatility) and risk management.
  3. Recognize the value and also the limits of econometric methods in financial time series.

Course Resources


  • A. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management: Concepts, Techniques, and Tools, revised ed., Princeton University Press, 2015. (this book should be available in the school bookstore)
  • R. S. Tsay, Analysis of Financial Time Series, 3rd Ed, John Wiley, 2010. (the electronic version of the second edition is accessible through the school library)

Additional References

  • E. Zivot and J. Wang, Modeling Financial Time Series with S-plus, 2nd Ed., Springer, 2005.
  • J. Campbell, A. Lo, and A. MacKinlay, The Econometrics of Financial Markets, Princeton University Press, 1997.
  • R. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practices, 2013. OTexts.


Grading Policies


The assignments must be submitted electronically through the course website.

For all the programming homeworks, students should send two uncompressed files: a report and an R program organized by questions. Please do not copy and paste large parts of the R program as part of the solutions. Create your tables with the R output whenever it is possible or copy small sections of the R program and EXPLAIN the results.

Do not send sections of your code or ask a complex homework question by email. I cannot debug your program or write a long explanation by email. However, you are welcome to ask any questions about the homework or any other issue related to this class during class, after class or during the office hours.

Software: R is the preferred software package for this class.

CFA Institute Online Ethics Course:

You should complete the seven modules, one for each Standard, of the CFA Code and Standards. This course is about ethical behavior in the global investment management industry. It is accessible for free in the following link: You should submit a full course certificate of completion to receive 5 points.

Grades and Evaluation:
  • Assignments: 10%
  • Completion of CFA Institute Online Ethics course: 5%
  • Participation: 5%
  • Midterm: 40%
  • Final Exam: 40%

Class policy: No late homework will be accepted.

Re-grades: If you dispute the grade received for an assignment, you must submit, in writing, your detailed and clearly stated argument for what you believe is incorrect and why. This must be submitted by the beginning of the next class after the assignment was returned. Requests for re-grade after the beginning of class will not be accepted. A written response will be provided by the next class indicating your final score. Be aware that requests of re-grade of a specific problem can result in a regrading of the entire assignment. This re-grade and written response is final; no additional re-grades or debate for that assignment.

Lecture Outline

Topic Reading
Week 1 Introduction to R, forecasting and goodness of fit Tsay 1, QRM 3
Week 2 Autoregressive and moving average models Tsay 2.1-2.5, QRM 4.1
Week 3 ARMA models, autocorrelation & forecasting. Tsay 2.6, QRM 4.1
Week 4 Unit root test. Seasonality and models with time series errors Tsay 2.6-2.8, QRM 4.1
Week 5 Volatility modeling: ARCH & GARCH Tsay 2.9, 3.1-3.5; QRM 4.2
Week 6 Alternative GARCH models Tsay 3.6-3.13
Week 7 High frequency data analysis Tsay 5
Week 8 Value at Risk and economic capital Tsay 7; QRM, 2.1, 2.3.1-2.3.4
Week 9 Value at Risk & extreme value theory Tsay 7; QRM 5.1-5.2 (optional)
Week 10 Market risk QRM 9.2
Week 11 Credit risk QRM 10.1
Week 12 Credit risk QRM 10.2
Week 13 Operational risk QRM 13.1
Week 14 Enterprise wide risk QRM 8.4-8.5; Nocco and Stulz, “Enterprise Risk Management: Theory and Practice.”