I am very excited to announce that the Applied Mathematics Department, in
cooperation with the Departments of Economics and Statistics, at the
University of Washington is offering a new online three course computational
finance certificate. This certificate was developed by Doug Martin and
myself and the courses are built around using R for computational finance.
The courses, offered one per quarter and summarized in detail below, are
Investment Science, R Computing for Computational Finance, and Portfolio
Construction and Risk Management.  All three courses are required for the
certificate but can also be taken individually. Full details, application
materials and pricing information can be found at
http://www.pce.uw.edu/prog.aspx?id=5508  .  A summary of the certificate
program is given below.

 

NEW ONLINE COMPUTATIONAL FINANCE CERTIFICATE

 

Fall, Winter, and Spring Quarters 2010-11

 

Overview

 

This new online certificate program is offered by the Department of Applied
Mathematics in partnership with the departments of Statistics and Economics
and begins in fall quarter 2010. The certificate is awarded to students who
successfully complete the online certificate curriculum consisting of the
following three courses: 

 

Course Title
Number               Quarter

Investment Science
STAT 591              Fall

R Computing for Computational Finance               AMATH 500        Winter

Portfolio Construction and Risk Management     STAT 549              Spring

 

For course details see the Online Courses section below. Each of the above
courses may be taken on a stand-alone basis subject to instructor approval.

 

Benefits

 

Students will benefit from this certificate program by acquiring the tools
to effectively manage financial investments, supported by the use and
development of R programs, and will be positioned to: (a) apply for an entry
level position in a quantitative asset management organization such as a
long-only quantitative portfolio management group, an absolute returns hedge
fund, a fund-of-hedge funds, an endowment, or a pension, (b) transfer to a
quantitative asset management job position in your current organization, (c)
manage your own investment using quantitative portfolio construction and
risk management methods, (d) continue on to a full MS degree in quantitative
finance in preparation for career advancement in the quantitative asset
management field. Certificate credits earned may be applied to an MS in
Computational Finance and Risk Management degree at the University of
Washington currently in the planning stage.

 

Curriculum Design

 

The Online Certificate curriculum was designed to provide the strongest
possible three-course introductory education in Computational Finance that
balances theoretical foundations, powerful modern computing tools and
investment portfolio management. The first course, Investment Science,
provides the financial, mathematical and statistical foundations needed for
sound investment decision making by covering three fundamental topic areas:
(a) interest rates and fixed income, (b) portfolio and factor model theory,
(c) futures, forwards and options. The quantitative level of this course is
intermediate between that of an MBA investments course and that of Ph.D.
level finance course. The second course, R Programming for Computational
Finance, leverages: (a) the overall strengths of the open source R
programming language for statistical modeling and data analysis as applied
to finance, (b) the rapid growth in use of R in quantitative finance, and
(c) rapid emergence of new R "packages" for both conventional and cutting
edge financial analytics. Finally, Portfolio Construction and Risk
Management, builds on the foundations of the first two courses to provide a
very modern approach to portfolio management that covers both conventional
mean-variance based portfolio methods and new cutting edge methods of
portfolio optimization and risk budgeting that take into account fat-tailed
skewed returns distributions. It also covers other modern approaches such as
volatility clustering and Bayesian methods and deals with practical
optimization such as weights constraints, turnover cost control, and
liquidity risk. The course involves hands-on computing and performance
comparisons using historical asset returns data from multiple financial data
service providers. Course outlines are provided at the above course title
links.

 

Background

 

This online certificate program is an out-growth of a successful Graduate
Certificate in Computational Finance program started in 2004 for resident
Ph.D. students in science and engineering. The program was founded by
Professors Doug Martin in Statistics and Eric Zivot in Economics, who serve
as Director and Co-director of the program. This program has approximately
20 students currently enrolled and has placed 9 students in finance industry
companies and 3 in university faculty positions since 2006. Brief resumes of
Martin and Zivot, along with that of Guy Yollin, who are collectively
teaching courses in the Online Computational Finance Certificate are
provided in the next section. For details on the overall Computational
Finance Program at University of Washington see:
http://www.amath.washington.edu/studies/compfin/ .

 

Application Process

 

For information on how to apply for this program and other details see the
following web site: http://www.pce.uw.edu/prog.aspx?id=5508  .

 

ONLINE CERTIFICATE FACULTY RESUMES

 

R. Douglas Martin

 

Martin is a Professor of Statistics, Adjunct Professor of Finance, and
Director of Computational Finance. He was Chair of Statistics during its
early formative years, a consultant at Bell Laboratories for ten years,
founder of the S-PLUS company Statistical Sciences, and founder and Chairman
of the risk management software company FinAnalytica, Inc. His numerous
publications on time series and robust statistical methods include one
Annals of Statistics and two Royal Statistical Society discussion papers. He
is co-author of Modern Portfolio Optimization (2005), and Robust Statistics:
Theory and Methods (2006), and frequent invited speaker at finance industry
conferences. His research focus is on applications of modern statistical
methods in portfolio construction and risk management. He holds the B.S.E.
and Ph.D. in Electrical Engineering from Princeton University

 

Eric Zivot

 

Eric Zivot is the Robert Richards Chaired Professor in the Economics
Department, Adjunct Professor of Statistics, and Adjunct Professor of
Finance. He regularly teaches courses on econometric theory, financial
econometrics and time series econometrics, and is the recipient of the Henry
T. Buechel Award for Outstanding Teaching. He was an associate editor of the
Journal of Business and Economic Statistics. He is co-author of Modeling
Financial Time Series with S-PLUS and co-developer of S+FinMetrics, and has
consulted on the use of S-PLUS and R in the finance industry. He has
published in the leading econometrics journals, including Econometrica,
Econometric Theory, the Journal of Business and Economic Statistics, Journal
of Econometrics, and the Review of Economics and Statistics, and in
empirical finance journals including the Journal of Empirical Finance, the
Journal of Financial Markets, and the Journal of International Money and
Finance. He holds the Ph.D. in Economics from Yale University.

 

Guy Yollin

 

Guy is a quantitative research analyst, risk manager, and R language
evangelist for Rotella Capital Management (RCM), a Seattle-area hedge fund
manager specializing in the trading of global futures and foreign exchange
markets. Prior to joining RCM, Guy led the quant finance software
development team at Insightful Corporation, developers of S-PLUSR and
S+FinMetricsR. Guy has given numerous talks on R/S programming for financial
applications and has taught graduate courses in statistical computing and
financial time series analysis. He holds a master's degree in computational
finance from the Oregon Graduate Institute (now part of Oregon Health &
Science University) and a bachelor's degree in electrical engineering from
Drexel University.

 

ONLINE CERTIFICATE COURSES

 

STAT 591 Investment Science

(Temporary course catalog title is Special Topics in Statistics)

. Quarter: Autumn Quarter 2010

. Time: Mon., Tues. 4:30 to 6:20

. Instructor: R. Douglas Martin

 

Overview

 

This course is an introduction to the mathematical and statistical
foundations and financial concepts of investment science. The material is
similar in scope to an MBA level investments course, but at a significantly
higher quantitative level.

 

Topics

 

. Basic Theory of Interest Rates. Compounding, present value, internal rate
of return

. Fixed Income Securities. Bonds, value formulas, yield, duration,
convexity, immunization

. Term Structure of Interest Rates. Bonds, PV, yield, duration, convexity,
immunization

. Mean-Variance Portfolio Theory. Diversification, efficient frontiers,
two-fund theorems,

. Factor Models. Multi-factor models, linear regression and prediction,
arbitrage pricing theory

. General Principles. Expected utility maximization, risk aversion, linear
and risk neutral pricing.

. Futures and Forwards. Futures and forward prices, margin, hedging with
futures

. Binomial Tree Derivative Pricing. Binomial models, no arbitrage and risk
neutral pricing

. Introduction to Options Theory. Brownian motion, Ito's lemma,
Black-Scholes, the Greeks

. Multi-Period Portfolio Management. Log-optimality, asset liability
management

 

Textbook

 

D. G. Luenberger (1998). Investment Science, Oxford University Press

 

Prerequisites

 

Familiarity with matrix algebra, solutions to linear equations, and calculus
through partial differentiation and constrained optimization using Lagrange
multipliers. Introductory probability and statistics at the level of STAT
390 or STAT/AMATH 506.

 

AMATH 500 R Programming for Computational Finance

(Temporary course catalog title is Special Studies in Applied Mathematics)

. Quarter: Winter Quarter 2011

. Time: Mon., Wed. 6:30 to 8:20

. Instructor: Guy Yollin

 

Overview

 

This course is an in-depth hands-on introduction to the R statistical
programming language (www.r-project.org) for computational finance. The
course will focus on R code and code writing, R packages, and R software
development for statistical analysis of financial data including topics on
factor models, time series analysis, and portfolio analytics.

 

Topics

 

. The R Language. Syntax, data types, resources, packages and history

. Graphics in R. Plotting and visualization

. Statistical analysis of returns. Fat-tailed skewed distributions,
outliers, serial correlation

. Financial time series modeling. Covariance matrices, AR, VecAR

. Factor models. Linear regression, LS and robust fits, test statistics,
model selection Multidimensional models. Principal components, clustering,
classification

. Optimization methods. QP, LP, general nonlinear

. Portfolio optimization. Mean-variance optimization, out-of-sample back
testing

. Bootstrap methods. Non-parametric, parametric, confidence intervals, tests

. Portfolio analytics. Performance and risk measures, style analysis

 

Textbooks

 

J. Adler (2009). R in a Nutshell: A Desktop Quick Reference, O'Reilly Media

D. Ruppert (2010). Statistics and Data Analysis for Financial Engineering,
Springer

 

Prerequisites

 

STAT 591 Investment Science, Introductory probability and statistics at the
level of STAT 390 or STAT/AMATH 506, or equivalents. Familiarity with matrix
algebra, multivariable calculus and optimization with Lagrange multipliers.
Basic computer programming experience.

 

STAT 549 Portfolio Construction and Risk Management

(Temporary course catalog title is Statistical Methods for Portfolios)

. Quarter: Spring Quarter 2011

. Time: Mon., Tues. 4:30 to 6:20

. Instructor: R. Douglas Martin

 

Overview

 

This computationally oriented course uses R and R+NuOPT for portfolio
construction and risk management. The course is unique in focusing on not
only classical mean-variance optimization methods but also on post-modern
optimization based on new downside risk measures for dealing with fat-tailed
and skewed distribution of asset returns.

 

Topics

. Portfolio risk analysis. Risk measures, incremental and marginal risk,
risk management

. Mean-variance and mean-risk. Old and new optimization methods

. Numerical portfolio optimization. Using R and R+NuOPT with real-world
constraints, penalties

. Estimation error. Classical sampling distribution methods and bootstrap
methods

. Active management. Alpha, benchmarks, information ratios, IC's and TC's

. Long-short portfolios. Market neutral versus dollar neutral, 130-30

. Fundamental factor models. Three types, optimization, risk management,
robust fitting

. Leverage. Types of leverage, return versus risk considerations

. Liquidity and market impact. Liquidity risk, Sadka risk beta, market
impact models

. Portfolio risk budgets. Volatility risk versus tail risk budgets, implied
returns

. Bayes methods. Bayes shrinkage, Bayes-Stein, Black-Litterman

 

Textbooks

 

Scherer and Martin (2011). Modern Portfolio Optimization (2011), 2nd edition
preprint, 

Connor, Goldberg and Korajczyk (2010), Portfolio Risk Management.

 

Prerequisites

STAT 591 Investment Science plus either ECON 424 or STAT 592 R Programming
for Computational Finance, or equivalents.

 

 

Eric Zivot                                               

Professor and Gary Waterman Distinguished Scholar       

Department of Economics                                 

Adjunct Professor of Finance                            

Adjunct Professor of Statistics

Box 353330                  email:  ezivot@u.washington.edu 

University of Washington    phone:  206-543-6715            

Seattle, WA 98195-3330
www:  http://faculty.washington.edu/ezivot                  

 


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