This course is an introduction to data analysis and econometric modeling using applications
in finance. Equivalently, this course is an introduction to
computational finance and financial econometrics. As such, the
course utilizes concepts from microeconomics, finance, mathematical
optimization, data analysis, probability models, statistical analysis
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) specification
of an economic model; (2) estimation of an econometric model; (3) testing
of the assumptions of the econometric model; (4) testing the
implications of the economic model; (5) forecasting from the econometric
model. The modeling process requires the use of economic theory, probability
models, optimization techniques and statistical analysis.
Topics in financial economics include asset return calculations, portfolio theory, index models,
the capital asset pricing model and investment performance analysis. Mathematical topics covered include optimization methods involving
equality and inequality constraints and basic matrix algebra. Statistical topics to be
covered include probability and statistics (expectation,
joint distributions, covariance, normal distribution, sampling
distributions, estimation and hypothesis testing etc.) with the use of
descriptive statistics and data analysis, linear regression, basic time series
methods, the simulation of random data
and resampling methods.
This course is
one of the core courses for the new Graduate Certificate in
Computational Finance. It is an elective for the
Undergraduate Certificate in Economic Theory and Quantitative
Methods and one of the core courses for
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
course is in the process of being renumbered and renamed in order to
form a sequence of upper division courses in financial economics. The
proposed new course number is Econ 424, and the proposed new name is
Introduction to Computational Finance. Econ 422 combined with Econ
424 provides a comprehensive introduction to the theory and practice of
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.
Homework assignments for the students taking the course as part of the
Graduate Certificate in Computational Finance will be more
difficult and more computationally intensive.
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).
Statistics and Finance: An Introduction, by David Ruppert,
Springer-Verlag, New York.
An Introduction to Computational Finance and
Financial Econometrics by Eric Zivot, manuscript in preparation
(see the Notes page for preliminary chapters)
The Basics of
Edition, by Krause and Olson, Springer-Verlag, New York. Available
for purchase from Insightful
Corp. This is a very nice introduction
to S-PLUS with lots of examples. A possible replacement for
this book is David Smith's short-course notes on S-PLUS for finance.
These are available for download:
Modern Portfolio Theory and Investment Analysis,
Sixth Edition, by E.J. Elton and
M.J. Gruber, Wiley, New York, 2002. This text
gives a very detailed treatment of portfolio theory.
Modeling, Second Edition, by Simon Benninga. MIT Press, 2000.
This textbook covers financial modeling using Microsoft Excel.
Statistical Analysis of Financial data in S-PLUS, by Rene Carmona,
Springer-Verlag, 2004. This is a great book but is a bit too advanced
for this course. It is used at Princeton in the Masters Program in
recommended texts will be placed on reserve at the library.
The course will utilize Microsoft Excel for spreadsheet
modeling, and S-PLUS for data analysis and statistical modeling.
Excel is included with all version of
Microsoft office, and is available on all PC computers around campus.
S-PLUS is a statistical modeling and
graphical analysis program sold by Insightful
Corporation (a local Seattle company), and is available on many
computers throughout the UW campus. The CSSCR lab, in the basement of
Savery Hall, has S-PLUS for windows (and the add-on modules) on most of
the PCs. S-PLUS for UNIX is also available on some of the campus
mainframe computers. Insightful provides a free student version of
S-PLUS through e-academy.
The University of Washington has an S-PLUS
site license package, which allows students to purchase the full version
of S-PLUS along with all add-on modules for $115. There is also a
discount for purchases of 5-pack bundles ($475 per 5-pack).
There are several add-on
modules for S-PLUS. We will utilize the
module for some of the statistical analysis. This module is not
included with the student version of S-PLUS, but is included in the
S-PLUS site license package. It is available on the
computers in the CSSCR lab, the Balmer Computer lab, the econ dept
computer lab and the MSCC computer lab.
Modeling Financial Time Series with S-PLUS by Eric Zivot and Jiahui
Wang, Springer-Verlag, serves as the User's Guide for S+FinMetrics. A
non-printable .pdf version of the book is available from the Insightful
Modeling Financial Time Series with S-PLUS