NEW ONLINE COMPUTATIONAL FINANCE CERTIFICATE

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1 NEW ONLINE COMPUTATIONAL FINANCE CERTIFICATE Fall, Winter, and Spring Quarters 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 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 threecourse 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

2 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 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: Application Process For information on how to apply for this program and other details see the following web site:

3 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-PLUS and S+FinMetrics. 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.

4 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 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.

5 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 This course is an in-depth hands-on introduction to the R statistical programming language ( 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.

6 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 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 meanvariance 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, 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.