Introduction to Computational Finance and Financial Econometrics
School: University of Washington
Registration Link: Introduction to Computational Finance and Financial Econometrics
Start Date: February 23, 2015 (10 Weeks)
Workload: Not Specified
Instructor: Eric Zivot
Credentials: A Yale Ph.D., Zivot has taught economics, statistics, finance, and applied mathematics courses at the University of Washington for 18 years. A consultant in the finance and software industries, Zivot conducts research on financial risk measurement and the econometric analysis of high frequency financial data. He has also received the university’s Henry T. Buechel Award for Outstanding Teaching.
Graded: Not Specified
Description: Using the R statistical programming language, students will learn how to “analyze financial data, estimate statistical models, and construct optimized portfolios.” In addition, students will create probability models to project asset returns and measure portfolio risk. Along with video lectures, students will complete assignments on Datacamp.com, which provides instant feedback on student computations.
Review: “Most of the class is spent in a detailed review of basic statistics, with an eye to applying it to financial data series. I really needed that, plus we were taught how to do all computations in R, with useful examples. Great treatment of confidence and the bootstrap methods. Final weeks were about basics of portfolio theory (efficient frontier, etc.) again enabling us to do all computations. I also appreciated the teacher mentioning that the theory’s value decreases when the market is unstable (as correlation increases) and showing how wildly the theoretic results can vary depending on when the data is collected. Overall I now feel confident with basic statistics (also beyond financial applications) and have continued to use R for statistics and data analysis since this class.” For additional reviews, click here.
Additional Note: Dr. Zivot recommends several texts before participating in the course, which are listed on the course link above.