ECON 424/CFRM 462:  Computational Finance and Financial Econometrics

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Course Description

Eric Zivot
348 Savery Hall
Office Hours: TuTh 3:30-4:30 (after class)

Winter 2016

This course is an introduction to computational finance and financial econometrics - data science applied to finance. The course covers computer programming and data analysis in R,  econometrics (statistical analysis), financial economics, microeconomics, mathematical optimization, and probability models. A free online version of this course is available on Coursera and has been taken by over 100,000 students world-wide.

The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data. This involves: (1) exploratory data analysis; (2) specification of models to explain the data; (3) estimation and evaluation of models; (4) testing the economic implications of the model; (5) forecasting from the model. The modeling process requires the use of economic theory, matrix algebra, optimization techniques, probability models, statistical analysis, and statistical software.

Topics in financial economics that will be covered in the class include:

  • asset return calculations

  • risk concepts

  • portfolio theory

  • risk budgeting

  • index (factor) models

  • capital asset pricing model

Mathematical topics covered include:

  • optimization methods involving equality and inequality constraints

  • basic matrix algebra

  • matrix differential calculus (sounds hard but it isn't)

Statistical (Econometric) topics to be covered include:

  • probability and statistics with the use of calculus

    • expectation, univariate and joint distributions, covariance,  normal distribution, etc.

  • Monte Carlo simulation

  • basic time series models

  • descriptive statistics and data analysis

  • estimation theory and hypothesis testing

  • resampling methods (e.g., bootstrapping)

  • linear regression 

  • data analysis using the open source R programming language

This course is an elective for the Undergraduate Certificate in Economic Theory and Quantitative Methods and one of the core courses for the new Certificate in Quantitative Managerial Economics. It is also included in the Advanced Undergraduate Economic Theory and Quantitative Methods Courses list for the Bachelor of Science degree in Economics.

ECON 424 is cross-listed with CFRM 462. Students entering the Professional MS in Computational Finance and Risk Management program or the Computational Finance Certificate program will benefit from being familiar with this ECON 424/CFRM 462 course material.

Course Requirements

  • Homework and Computer labs 25%: due every Tuesday by 8 pm PST (submitted online via Canvas)

  • 1 Midterm exam 25% (tentatively scheduled for )

  • Class project 25% - W credit will be given if you receive a grade of 3.3 or higher on the class project (Due Friday March 11 at 8 pm via Canvas)

  • Final Exam 25% ()

The homework, computer labs and project comprise the core of the course and have been weighted accordingly for grading purposes.  I believe that one cannot obtain an adequate knowledge and appreciation of model building, finance and econometrics without "getting one's hands dirty" in the computer lab.


Formally, the prerequisites are Econ 300 and an introductory statistics course (Econ 311 or equivalent). Econ 482 (Econometric Theory) is not a prerequisite. More realistically, the ideal prerequisites are a year of calculus (through partial differentiation and constrained optimization using Lagrange multipliers), some familiarity with matrix algebra, a course in probability and statistics using calculus, intermediate microeconomics and an interest in financial economics (Econ 422 would be helpful).

Required Texts

  • An Introduction to Computational Finance and Financial Econometrics with R by Eric Zivot, manuscript in preparation. Book manuscript is posted on the Canvas syllabus page. Older versions of the notes are on the notes page.

  • Statistics and Data Analysis for Financial Engineering, Second Edition by David Ruppert and David Matteson, Springer-Verlag.  Book website. The UW library has access to the UseR series of books from Springer-Verlag. If you have a UW net ID then you can get access to these ebooks through the UW library page. If you are connecting from a computer that is off campus be sure to use the Off Campus login link. A direct link to Statistics and Data Analysis for Financial Engineering is here.

  • A Beginner's Guide to R by Alain Zuur, Elena Ieno and Erik Meesters, Springer-Verlag. A direct link to A Beginner's Guide to R is here

  • R Cookbook by Paul Teetor, O'Reilly.

Recommended Texts


The course will utilize R for data analysis and statistical modeling and Microsoft Excel for spreadsheet modeling.

Excel is included with all version of Microsoft office, and is available on all PC computers around campus.

R is a free open-source statistical modeling and graphical analysis language built upon the S language developed at Bell Labs and is available on many computers throughout the UW campus. It can be downloaded from There are versions available for the PC, Mac and various forms of LINIX. The CSSCR lab, on the 1th floor of Savery Hall, has R on most of the PCs. I highly recommend using RStudio ( as a free integrated development environment for R (runs on windows, MAC and LINUX).

We will be using several user-created packages (libraries of R functions) specifically designed for the analysis of financial time series data. R packages are maintained on the web and can be automatically downloaded from with R. The R package IntroCompFinR is the companion package for my book An Introduction to Computational Finance and Financial Econometrics with R and is available on R-Forge here. This package contains data for all of the examples in the book as well as a number of useful functions for data, portfolio and risk analysis.