AMATH 510 Financial Data Access & Analysis with SQL, VBA, Excel (4)
Working financial analytics practitioners regularly need to access data stored in SQL databases. In addition, it is common for the results of an analysis to be summarized and distributed via an Excel spreadsheet. This course provides practical lessons in the retrieval and manipulation of data using SQL, VBA, and Excel. In addition it shows how to leverage the powerful financial data modeling and analysis capabilities of R in conjunction with use of SQL, VBA and Excel. Course topics include:
- SQL query development
- SQL database access from R via DBI and RODBC
- Data analysis with PowerPivot
- Excel VBA object model and VBA procedure development
- SQL database access from VBA
- Web data access from VBA
- Excel and R interoperability
- VBA client and R server computing
Course will include a project that involves an end-to-end implementation of an analytic solution that emulates the type of implementation using SQL, VBA and Excel that will be required by a finance industry organization.
Instructor: Guy Yollin
Textbooks: Sams Teach Yourself SQL in 24 Hours, Second Edition, R. Plew and R. Stephens
Excel 2007 VBA Programmer's Reference, J. Green
DAX Formulas for PowerPivot, R. Collie
Software: Microsoft Excel, SQLite, R
Prerequisites: AMATH 463 Foundations of R Programming or equivalent, or by permission.
AMATH 540 Capital Markets and Data for Computational Finance (2)
This course has a dual purpose. One purpose is to introduce students, many of whom are coming from engineering and science backgrounds, to the language, terminology and concepts of finance. This will be done through selected readings in the textbook “Investments" by Bodie, Kane and Marcus, and selected readings of journal articles and newspapers/websites such as the Wall Street Journal, Financial Times and Bloomberg. The second purpose is to teach students how to access and manipulate a broad range financial and econometric data from various repositories available through the internet (e.g. FRED, WRDS, CRSP, Bloomberg, Russell, etc.) for use in subsequent MS-CFRM curriculum courses. Upon successful completion of the course the student will:
- Know how to screen price and returns data for the following asset classes: equities, fixed-income, mutual funds, ETF’s, commodities, futures, hedge funds, indexes
- Understand the different money market instruments available for investment
- Understand the basics of mutual funds
- Be familiar with a variety of equity indices and their construction methods
- Understand the varieties of financial ratios used in assessing a firm’s credit rating
- Be able to use dividend discount models for valuing equities
Course Dates: September three-week early-start (Sept. 3 - 24, 2013)
Instructor: Steve Golbeck
Textbook: Bodie, Kane and Marcus, Investments
Prerequisites: Basic mathematics and familiarity with finite and infinite series.
AMATH 541 Investment Science (4)
This course is an introduction to the mathematical, statistical and financial foundations of investment science. Learning of the theoretical concepts will be re-enforced through use of R computing exercises. The material is similar in scope to an MBA level investments course, but at a significantly higher quantitative level. Topics include:
- 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 (discount factors, forward rates, short rates)
- Mean-Variance Portfolio Theory (efficient frontiers, quadratic utility, benchmark tracking)
- Factor Models (CAPM, linear regression and prediction, multi-factor models, APT)
- General Principles (expected utility maximization, coherent and tail risk measures)
- Futures and Forwards (futures and forward prices, margin, hedging with futures)
- Options Part 1: (option payoffs, trading strategies, binomial models, risk neutral pricing)
- Options Part 2: (Ito process and lemma, GBM, Black-Scholes, hedging, implied volatility)
Instructor: Eric Zivot
Textbook: To be determined
Software: R and R Finance Packages
Prerequisites: Coursework in multivariate calculus, linear algebra, and one-dimensional optimization at the level of AMATH 460, and probability and statistics at the level of AMATH 461, or permission of instructor. Familiarity with the material in AMATH 462 is desirable.
AMATH 542 Financial Data Modeling and Analysis in R (4)
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 include:
- 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
Instructor: Guy Yollin
Textbooks: D. Ruppert (2010). Statistics and Data Analysis for Financial Engineering, Springer and J. Adler (2009). R in a Nutshell: A Desktop Reference, O’Reilly Media
Software: R and R packages.
Prerequisites: AMATH 541 Investment Science or equivalent, and competency in R at the level of AMATH 463, or permission of instructor.
AMATH 543 Portfolio Optimization and Asset Management (4)
As a consequence of changes in the MS-CFRM curriculum whereby certain aspects of portfolio optimization and risk models and analysis have already been covered in previous courses, the content of AMATH 543 is changing in important ways this spring quarter 2013. The list below indicates the topics to be covered, with approximately one lecture devoted to each topic. The content coverage will blend theory and analytics with computational methods implementation in R code. The latter supports hands-on portfolio construction and analysis exercise portions of homework assignments.
- Constrained long-only and long-short mean-variance portfolio optimization
- Back-testing, portfolio performance analysis and estimation error
- Expected tail loss/conditional value-at-risk portfolio optimization theory and computing
- Expected utility and coherent risk measure portfolio optimization
- Active/benchmark relative portfolio optimization theory and computation
- Information ratios and coefficients and fundamental law of active management
- Covariance estimation: classical, EWMA, robust, shrinkage, unequal histories methods
- Fundamental factor models: risk analysis and alpha forecasts
- Hedge funds: types, special problems, time series factor models, replication
- Statistical factor models and PCA for risk analysis and forecasting
- MID-TERM EXAM
- Marginal contributions to risk and implied returns guided asset allocation
- Differential evolution optimization for “hard” problems. Random portfolios.
- Risk-parity portfolios, equal weight portfolios, volatility pumping and Kelly betting
- Leverage: Types of leverage, return versus risk considerations
- Liquidity and market impact: Sadka liquidity risk beta (extra lectures by Sadka)
- Introduction to Bayes methods in finance
- Bayes-Stein alpha estimates and Bayes shrinkage covariance estimates
- Basic Black-Litterman model and its calibration. Meucci extensions
- Additional Bayes topics TBD
Instructor: R. Douglas Martin
Textbooks: Chincarini and Kim (2006). Quantitative Equity Portfolio Management, McGraw-Hill. (order from book provider of your choice, currently $38.95 from Amazon); Martin, Yollin and Scherer (2013). Modern Portfolio Optimization, selected 2nd edition draft chapters to be provided by instructor.
Software: R and R packages including PerformanceAnalytics and PortfolioAnalytics
Prerequisites: AMATH 541 Investment Science and AMATH 542 Financial Data Modeling and Analysis in R, or equivalents, or by permission of instructor.
AMATH 544 Options and Derivatives (4)
This course provides basic knowledge of the theory, statistical modeling and computational methods of pricing options and other derivative products. The course blends mathematical and statistical theory with hands-on computing. The first part of the course will emphasize options on stocks, stock indices, currencies and futures, and the latter part will focus on interest rate derivatives. Course work includes assignments in theory and computation, and either a final exam or a project.
- Brief review of forwards, futures, and options basics
- Black-Scholes theory and dynamic hedging with the Greeks
- Volatility estimation, implied volatility, the volatility smile
- Option prices using additive and multiplicative binomial, and use of trinomial trees
- Option pricing under fat-tailed non-normality
- Computational methods for exotic options and complex derivatives
- Brief review of interest rate basics: zero rates, forward rates and term structure Interest rate derivatives: standard market models, short rate and advanced models
- Analytic models and tree models for pricing interest rate derivatives
- Valuation of bonds with embedded options, option adjusted spreads
Instructor: Steve Golbeck
Textbooks: Hull, J. C. (2009). Options, Futures and Other Derivatives, 7th edition (or most recent edition available at time of course offering), Prentice Hall.
Tuckman, B. (2002). Fixed Income Securities, 2nd edition, Wiley
Software: R and selected R packages
Prerequisites: AMATH 540/ECON 424 Introduction to Computational Finance and Financial Econometrics and AMATH 541 Investment Science coverage of forwards, futures and options, or equivalent. AMATH 542 Financial Data Modeling and Analysis in R is desirable.
AMATH 545 Risk Management I (4)
This introductory risk management course covers the methodologies used to manage financial risk. Emphasis is given to fixed income and foreign exchange derivatives. The topics covered include:
- An overview of fixed income products.
- Duration and convexity and risk management of fixed income portfolios.
- Black and Scholes model. Hedging and trading parameters.
- Pricing options and swaps.
- Introduction to term structure models.
- Introduction to credit derivatives.
- Introduction to mortgage-backed securities and asset-backed securities.
- Introduction to hedge fund strategies and risk management.
Instructors: Mark Everitt (Blackrock) and John McMurray(Russell Investments)
Textbooks: Assigned readings.
Software: Microsoft Excel
Prerequisites: AMATH 541 Investment Science or equivalents, or by permission.
AMATH 546 Risk Management II (4)
This is a course in quantitative risk management and financial econometrics. The focus will be on the statistical modeling of financial time series (asset prices and returns) with an emphasis on modeling volatility and correlation for quantitative risk management. The learning goals/objectives of the course are to (1) survey the relevant theoretical and practical literature; (2) introduce state-of-the-art techniques for modeling financial time series and managing financial risk; (3) use the open source R statistical software to get hands-on experience with real world data. Topics to be covered include:
- Empirical properties and stylized facts of asset returns
- Probability distributions and statistical models for asset returns
- Risk concepts
- Volatility modeling
- Extreme value theory
- Multivariate dependence using copulas
- Introduction to credit risk models and management
Instructor: Eric Zivot
Textbooks: McNeil, Frey, and Embrechts, Quantitative Risk Management: Concepts, Techniques, and Tools, University Press, 2005. (Required)
Jondeau, E., Poon, S.-H., and Rockinger, M. (2006). Financial Modeling Under Non-Gaussian Distributions, Springer-Verlag. (Instructor will show how to obtain this book online at no charge)
Software: R and R Finance Packages
Prerequisites: AMATH 542 Financial Data Modeling and Analysis in R and its pre-requisites, or equivalent.
AMATH 547 Credit Risk Management (4)
This course is an introduction to the mathematical, statistical and financial foundations of models for analyzing, predicting, and mitigating credit risks. Students will learn the theoretical basis for widely-used modeling methods for credit risk assessment and implement those methods through programming assignments using R. The course will focus on both obligor-level and portfolio-level credit risks, as well as valuation and risk analysis of assets and derivatives with credit risk. Topics include:
- Credit risk drivers and portfolio diversification (idiosyncratic and systemic risks)
- Applied logistic regression (credit scoring models)
- Credit rating products for individuals and corporations (FICO, S&P, Moodys, Experian)
- Merton model for default risk
- Credit risk economic capital
- Basel II credit capital framework for banks
- Modeling loss frequency (PD) and severity (LGD)
- Credit risks in structured asset backed securities
- Credit default swaps, models for valuation and risk measurement
Instructor: Jay Henniger
Textbook: Servigny and Renault (2004). Measuring and Managing Credit Risk, McGraw-Hill Professional
Software: R and R Finance Packages
Prerequisites: AMATH 541 and AMATH 546 or equivalents, or by permission
AMATH 548 Monte Carlo Methods in Finance (4)
This course covers a broad range of standard and specialized Monte Carlo methods in finance with a focus on accurate derivative pricing. Students will learn the theoretical rationale for the methods and will gain applications knowledge through programming assignments using R or Matlab. The course will begin with an overview Monte Carlo methods and a review of basic derivative pricing method. Topics covered will include:
- Derivative pricing methods: replication, no-arbitrage, risk-neutral pricing, change of numeraire
- Random number generators: linear congruential generators, lattice structure, simulation error
- Sampling methods: inverse transform, acceptance-rejection methods
- Mulivariate random numbers: normal distributions, t-distributions, stable distributions
- Simulating sample paths: univariate and multivariate GBM, path-dependent options, short-rate models and bond prices
- Simulating advanced models: square-root diffusions and bond prices, forward rate models and pricing derivatives, jump processes
- Variance reduction methods: antithetic variables, control variates, stratified sampling, Latin hypercube sampling, matching methods, importance sampling
- Discretization methods: Euler method, second-order methods, applications to extremes and barrier crossings
- Estimating the Greeks sensitivity measures: finite-difference approximations, pathwise derivative estimates, likelihood ratio method
- Pricing American options: random tree methods, stochastic mesh methods, regression methods
- Risk management applications: calculating VaR and CVaR, calculating VaR and CVaR portfolio risk decompositions, delta-gamma based variance reduction, methods for fat-tailed distributions
Instructor: Steve Golbeck (offered summer of 2013 and subsequently in spring quarter 2014)
Textbook: Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering, Springer
Software: R and R Finance Packages or Matlab
Prerequisites: AMATH 544 or equivalent, or by permission.
AMATH 551 Introduction to Trading Systems (3)
This course is an introduction to financial markets, instruments, exchanges, and the electronic trading process. Students will use paper trading accounts with both Interactive Brokers (www.interactivebrokers.com) and TradeStation (www.tradestation.com) to explore electronic trading of stocks, futures, and ETFs. Additionally, students will use the TradeStation Platform as well as the R language for statistical computing (www.r-project.org) to develop, evaluate, backtest, and optimize quantitative trading strategies. Topics include:
- Asset classes, financial instruments, and trade orders
- Direct access trading and market microstructure
- Interactive Brokers, Traders Workstation, and the IB Student Trading Lab
- Quantitative trading strategy development with the TradeStation Platform
- Quantitative trading strategy with R and the quantstrat package
- Trading strategy evaluation, optimization, and backtesting
Instructor: Guy Yollin
Textbooks: Algorithmic Trading and DMA: An introduction to direct access trading strategies by Barry Johnson, 4 Myeloma Press, 2010;
Trading Systems: A new approach to system development and portfolio optimization by Emilio Tomasini and Urban Jaekle, Harriman House, 2009
Software: R language for statistical computing; Interactive Brokers Traders Workstation; TradeStation Platform (requires MS Windows operating system)
Prerequisites: AMATH 462 or free Coursera version (may be taken concurrently, or by permission).
AMATH 552 Portfolio Performance Analysis & Benchmarking (2)
This course covers fundamental principles of portfolio performance measurement and benchmarking. Topics include:
- The role of performance evaluation in portfolio management
- Rate of return calculations for individual assets and for portfolios
- Manipulating returns: linking, averaging, annualizing
- Adjustments for inflation, currency, taxes, fees
- Cash flow methods: time-weighted returns, money-weighted returns, standard approximations
- Excess returns, arithmetic and geometric
- Sector-based performance attribution
- Volatility and asset pricing-based risk measures
- Risk-adjusted return measures
- Factor-based performance attribution
- Uses of indexes: benchmarking, asset allocation, and the basis for investment vehicles
- Benchmark construction principles and practical issues
- Index calculations, weighting, rebalancing, and maintenance
- Equity style indexes
- GIPS: Global Investment Performance Standards
Instructor: David R. Cariño
Textbook: J. A. Christopherson, D. R. Cariño, and W. E. Ferson (2009). Portfolio Performance Measurement and Benchmarking, New York: McGraw-Hill
Software: Spreadsheet applications and R
Prerequisites: AMATH 540/ECON 424 Introduction to Computational Finance and Financial Econometrics or equivalent, and AMATH 541 Investment Science or equivalent.
AMATH 553 Financial Time Series Forecasting Methods (4)
This course is an introduction to the role that forecasts can play in investment decisions, especially investing that involves views on short-term opportunities that are implemented through informed rebalancing or explicit asset class tilts away from benchmark. Learning of the theoretical concepts will be re-enforced through use of computing exercises. Topics include:
- Types of forecasts, dynamic forecasts, direct forecasts
- Forecasts by simulation for nonlinear models
- The role of macroeconomic forecasts in investing
- An approach to macroeconomic forecasting
- Asset class returns forecasts
- Ways to combine forecasts using dynamically updated weights
- Ways to account for nonlinearity
- Foreign exchange (FX) forecasts: carry trade motive, momentum strategies, incorporating long-run valuation correction
Instructors: Michael Dueker (not offered in 2013-14)
Prerequisites: AMATH 542 or equivalent, or permission.
AMATH 554 Endowment and Institutional Investment Management (2)
The course will focus on the endowment management process and specific challenges facing institutional fund managers. These include evaluating the role of an endowment, portfolio construction, risk management, manager selection, and alternative asset class investing. As such, the course utilizes concepts from finance and investments, macroeconomics, and mathematical optimization. Specific topics include:
- Endowment policy background and philosophy
- Risk and asset allocation
- Emerging market investing
- Fixed incomes role in endowment
- Liquidity and investing in private equity.
Reading assignments will form the basis for class discussion and students are expected to be prepared for case discussions.
Instructors: Yindeng Jiang, Garth Reistad, Keith Ferguson, and industry guest lecturers.
Textbooks: There is significant amount of reading for this course, including articles and investment research from multiple sources that will be assigned by the instructors.
Software: R will be useful in the event of some case applications.
Prerequisites: AMATH 541 Investment Science or equivalent. A general understanding of economics and a good background in core finance and portfolio optimization, e.g., AMATH 543 is recommended.
AMATH 555 Optimization Methods in Finance (4)
This course provides an introduction to numerical optimization methods in finance. The course will discuss the theory and efficient solution methods for major classes of optimization problems. Theoretical concepts will be paired with example applications and computing exercises. Homework problems will include use of an industrial strength optimizer to solve finance applications. Topics include:
- Linear Programming Theory, Algorithms and Applications: feasible sets, duality, optimality conditions, simplex method, interior point methods, sensitivity analysis, asset/liability cash flow matching
- Quadratic Programming Theory, Algorithms and Applications: constrained and unconstrained programming, optimality conditions, solution methodologies, mean-variance optimization, relationships to statistical regression, Black-Litterman, returns-based style analysis, risk-neutral density estimation
- General Non-Linear Programming Theory, Algorithms and Applications: univariate and multivariate models, convexity, non-smooth optimization, GARCH model fitting, volatility surface estimation
- Integer Programming Theory, Algorithms and Applications: cutting plane methods, index replication
- Combinatorial and Network Programming Theory, Algorithms and Applications: shortest path, min-cost flow, foreign exchange, arbitrage checking
- Cone Programming Theory, Algorithms and Applications: second-order cone programming, tracking error and volatility constraints, estimating covariance matrices
- Dynamic Programming Theory, Algorithms and Applications: Bellman equations, forward and backward recursion, knapsack problem, option pricing, structured products
- Stochastic Programming Theory, Algorithms and Applications: data uncertainty, multi-stage models, recourse, value at risk, conditional value at risk, asset/liability management, CVaR, transaction costs
- Robust Optimization Theory, Algorithms and Applications: parameter uncertainty, robust constraints, robust objectives, single-period and multi-period portfolio selection
- Additional Topics: Decomposition and Column Generation, Genetic Algorithms, Non-gradient methods
Instructor: Steven Murray
Textbook: Cornuejols and Tutuncu (2007). Optimization Methods in Finance, Cambridge University Press.
Software: R, open source optimizers. Other commercial portfolio optimization products such as CPLEX and Axioma, arrangements with vendors permitting.
Prerequisites: AMATH 542 or equivalent , or by permission. AMATH 543 Portfolio Construction and Risk Analysis is desirable.
AMATH 556 Statistical Modeling for Computational Finance (4)
This course covers the theory and application of statistical models and machine learning methods commonly used in quantitative and computational finance. Students in the MS-CF&RM program take this course during their first quarter and are expected to be able to use these modeling methods in subsequent courses. Computing exercises are used to reinforce the theory and methods. Topics covered include the following:
- Principal component analysis (PCA)
- Least-squares linear regression model fitting and model selection
- Modern shrinkage fitting methods for models with many predictor variables
- Robust linear regression model fitting and robust PCA
- Non-parametric regression methods
- Applications to factor models for asset returns
- Clustering and classification methods
Upon successful completion, students will be able to apply models from statistics and machine learning to a variety of financial applications, in particular to fixed income and portfolio optimization models.
Instructor: Kjell Konis
Textbooks: Hastie, Tibshirani and Friedman (2008). Elements of Statistical Learning, 2nd edition (download free PDF) and other textbook TBD.
Software: R and R packages.
Prerequisites: AMATH 541 Investment Science, which may be taken concurrently, or equivalent or by permission.
AMATH 557 Financial Software Development and Integration with C++ (4)
This course is a practical introduction to C++ programming for financial applications. The course will focus on developing basic object oriented programming skills in C++ to implement computational finance solutions. Course coverage will also include integrating C++ applications with R and Excel. Course topics include:
- C++ language, syntax, and control
- Object-oriented programming
- The C++ Standard library
- Rcpp interface from R to C++
- Rinside interface from C++ to R
- xlw interface from Excel to C++
- COM interface with C++
Instructor: Guy Yollin
Textbook: Starting Out with C++: Early Objects (7th Edition), Tony Gaddis, Addison-Wesley, 2010
Software: Microsoft Visual Studio 2010
Prerequisites: AMATH 542 and 544 which may be taken concurrently, or by permission.
AMATH 558 Fixed Income Analytics and Portfolio Management (4)
This required course in the MS-CFRM program provides a solid foundation in fixed income analytics and portfolio management. Course will include some lectures on real-world fixed income applications by finance industry professional guest lecturers. Computing exercises with R will be used throughout to re-enforce understanding of the theory and methods. Topics covered will include:
- Fixed income instrument types including MBS’s and municipal bonds
- Fixed income data sources, access and manipulation
- Term structure of interest rates and yield curve construction
- Interest rate risk management
- Interest rate forwards, swaps, futures and options
- Introduction to binomial tree pricing of interest rate derivatives
- Case studies.
Upon successful completion, students will have a firm understanding of fixed income markets, data and analytics, and be able to apply this knowledge to fixed income portfolio construction, performance analysis and risk management.
Instructor: Steve Golbeck or Doug Martin
Veronesi, P. (2010). Fixed Income Securities: Valuation, Risk and Risk Management, Wiley.
Tuckman, B. and Serrat, A. (2012) Fixed Income Securities: Tools for Today’s Markets, 3rd edition, Wiley.
Software: R and R packages for fixed income
Prerequisites: Good understanding of multivariable calculus, linear algebra, probability, and statistics at least at the level of AMATH 460 and 461.
AMATH 559 Stochastic Calculus for Derivatives (4)
** * Description will be added ***
AMATH 582 Computational Methods for Data Analysis (5)
AMATH 583 High Performance Scientific Computing (5)
AMATH 600 Independent Research or Study (variable credit)