FIN 620 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.
By the end of this course, the students will be able to:
- Understand the foundations of financial time series data, including high-frequency data
- Apply models and methods for analysis of financial time series (return and volatility) and risk management.
- Recognize the value and also the limits of econometric methods in financial time series.
- 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)
- 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)
- 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. https://www.otexts.org/fpp
The assignments must be submitted electronically through the course website.
For all the programming tasks, students should send two uncompressed files: a report and
an R program. 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 projects, homework, or any other issue related to this class during class or during the office hours.
Software: R is the preferred software package for this class. Occasionally, we will use Eviews too.
||Introduction to R, forecasting and goodness of fit
||Tsay 1, QRM 3
||Autoregressive and moving average models
||Tsay 2.1-2.5, QRM 4.1
||ARMA models, autocorrelation & forecasting.
||Tsay 2.6, QRM 4.1
||Unit root test. Seasonality and models with time series errors
||Tsay 2.6-2.8, QRM 4.1
||Volatility modeling: ARCH & GARCH
||Tsay 2.9, 3.1-3.5; QRM 4.2
||Alternative GARCH models
||High frequency data analysis
||Value at Risk and economic capital
||Tsay 7; QRM, 2.1, 2.3.1-2.3.4
||Value at Risk & extreme value theory
||Tsay 7; QRM 5.1-5.2 (optional)
||Enterprise wide risk
||QRM 8.4-8.5; Nocco and Stulz, “Enterprise Risk Management: Theory and Practice.”