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CFRM 405 Mathematical Methods for Quantitative Finance (3) NSc, RSN
Covers selected mathematical methods needed to begin a master's program in quantitative finance. Topics include applications of calculus, linear algebra, and constrained optimization methods to fixed income, portfolio optimization, futures, options, and risk management. Prerequisite: either AMATH 352, MATH 136, or MATH 208.

CFRM 410 Probability and Statistics for Computational Finance (3)
Covers basic concepts and methods of probability and statistical analysis and modeling for computational and quantitative finance. Coverage is carefully aligned with leading problems concerning prices and returns of individual assets and portfolios of assets. Key applications include financial risk management and portfolio performance analysis. Prerequisite: CFRM 405.

CFRM 415 Introduction to Financial Markets (3)
Introduction to fundamentals of investment science and financial derivatives. Topics include basics of interest rates and present value calculations, fixed income securities, term structure of interest rates, the concept of financial arbitrage, pricing of futures, forwards, and call/put options, binomial lattice model, portfolio theory, and capital asset pricing model. Prerequisite: CFRM 405 and CFRM 410, may be taken concurrently.

CFRM 420 Introduction to Computational Finance and Financial Econometrics (3)
Covers probability models, data analysis, quantitative, and statistical methods using applications in finance, and introduction to and use of the R programming system for data analysis and statistical modeling. Course overlaps with: ECON 424. Prerequisite: CFRM 405, CFRM 410, or instructor permission.

CFRM 421 Machine Learning for Finance (4)
Fundamentals of machine learning techniques with applications to finance. Assessing, organizing, and analyzing financial data, and learning the analytical tools and numerical schemes in machine learning to perform statistical analysis on financial data. Develops practical financial tools such as trading rules and risk indicators. Prerequisite: CFRM 405 and CFRM 410.

CFRM 422 Introduction to Trading Systems (4)
Introduces electronic trading systems. Uses the R programming language to develop, evaluate, and optimize quantitative trading strategies. Students apply trading strategies through a live paper-trading account with an online broker using real time market data. Prerequisite: CFRM 420 and CFRM 425.

CFRM 425 R Programming for Quantitative Finance (3)
Introduction to R programming language for applications in quantitative finance. Covers R syntax, data structures and manipulation, data analysis and statistics. Working with time series and computing asset returns with R will be covered, as will be the R package system and contributed packages. Prerequisite: CFRM 405; CFRM 410; CFRM 415; and CFRM 420.

CFRM 426 FinTech, Blockchains, and Cryptocurrencies (4)
Financial technology (FinTech) innovations and development, and the associated computational finance and risk-management methods and perspectives. Real-world applications, including robo-advising, AI and Machine Learning for trading. Also, blockchain technology with focus on applications to finance, especially cryptocurrencies. Prerequisite: CFRM 415 and CFRM 425; recommended: ability to program and compile in R and/or Python.

CFRM 430 Fixed Income Analytics (4) NSc
Covers fixed income markets and securities, data sources, analytics and portfolio management methods, in particular the valuation, risks, and risk management of fixed income securities. Uses a hands-on data-oriented and computational focus. Course overlaps with: FIN 467. Prerequisite: CFRM 405; CFRM 410; and CFRM 415.

CFRM 442 Credit Risk Management (4) NSc
Theory, applications and computational methods for credit risk measurement and management. Statistical and mathematical modeling of credit risk, emphasizing numerical methods and R programming. Methods include logistic regression, Monte Carlo simulation, and portfolio cash flow modeling. Covers default risk regression, analytics, and portfolio models of credit risk. Prerequisite: CFRM 405 and CFRM 410.

CFRM 450 Stochastic Calculus for Quantitative Finance (4)
Provides a systematic examination of financial derivatives pricing using stochastic calculus. Examines popular stochastic differential equation models such as Geometric Brownian motion, Vasicek, Hull-White, Cox-Ingersoll-Ross, Black-Karasinski, Heath-Jarrow-Morton, and Brace-Gatarek-Musiela, as well as Poisson and Levy processes. Applications include equity, fixed-income, and credit derivatives. Course overlaps with: MATH 493/STAT 493. Prerequisite: CFRM 415.

CFRM 500 Special Studies in Computational Finance and Risk Management (1-6, max. 18)
Lecture and discussions of topics of current interest in computational finance and risk management. Prerequisite: permission of instructor.

CFRM 501 Investment Science (4)
Introduction to the mathematical, statistical and financial foundations of investment science. Topics include: utility functions, mean-variance portfolio theory, tail risk measures, factor model types for portfolio construction, classical and robust methods of fitting factor models, and covariance and correlation estimation. Prerequisite: CFRM 425. Offered: A.

CFRM 502 Financial Data Science (4)
Covers applications of statistical techniques for analyzing financial data, as well as modeling and computational methods in key areas in quantitative finance. Includes factor modeling, financial time series, and portfolio analytics. Focuses on advanced topics in statistical finance, finance theory, and financial applications. Prerequisite: CFRM 501.

CFRM 503 Asset Allocation and Portfolio Management (4)
Covers long-only and long-short portfolio optimization with real-world constraints and costs using industrial strength optimization software; classical mean-variance and modern mean-versus downside risk optimization for dealing with fat-tailed skewed asset returns; optimization and risk analysis with factor models; and equity, mixed asset class, and fund-of-hedge portfolios. Prerequisite: either CFRM 501 and CFRM 502, or permission of instructor. Offered: S.

CFRM 504 Options and Other Derivatives (4)
Covers financial instrument options and derivatives. Explores how to price options and other derivatives and use them to hedge investment risk. Involves theory, statistical modeling, numerical methods, and computation using the R programming language. Prerequisite: co-requisite: CFRM 501 or permission of instructor. Offered: A.

CFRM 505 Monte Carlo Methods in Finance (4)
Monte Carlo simulations in quantitative finance for portfolio assembly and financial risk management. Students learn theory and methods of tracking the behavior of underlying securities in an option or portfolio and determine the derivative's value by taking the expected value of the discounted payoffs at maturity. Prerequisite: CFRM 501. Offered: A.

CFRM 506 Financial Data Access and Analysis with SQL, VBA, and Excel (4)
Provides skills in retrieving and manipulating financial data and in creating computational solutions to quantitative finance problems using SQL, VBA, and Excel. Also teaches skills in leveraging the powerful financial data modeling and analysis capabilities of R in conjunction with SQL, VBA, and Excel. Prerequisite: either CFRM 501 or equivalent, or permission of instructor. Offered: A.

CFRM 507 Optimization Methods in Finance (4)
Covers theory and efficient solution methods for optimization problems in finance. Includes financial solution methodologies using linear, non-linear, quadratic, and integer formulations; and dynamic and stochastic programming. Prerequisite: Linear algebra and matrix notation; statistics and probability; and experience with R language and MS Excel. Offered: A.

CFRM 509 Ethics in the Finance Profession (2)
Addresses ethical theory to recognize and demonstrate an applied understanding of ethical conduct in financial markets, financial management and financial services. Explore assessments of, and responses to, ethical challenges in finance. Includes financial law and regulation.

CFRM 520 Financial Software Development and Integration with C++ (4)
Practical introduction to C++ programming for financial applications. Focuses on developing basic object oriented programming skills in C++ to implement computational finance solutions. Also includes integrating C++ applications with R, MATLAB, SQL, and VBA.

CFRM 521 Machine Learning for Finance (4)
Introduces the fundamentals of machine learning techniques with applications to finance. Focuses on assessing, organizing, and analyzing financial data, and learning the analytical tools and numerical schemes in machine learning to perform statistical analysis on financial data. Develop practical financial tools such as trading rules and risk indicators. Prerequisite: CFRM 502 or equivalent, which may be taken concurrently; programming skills in R or MATLAB.

CFRM 522 Introduction to Trading Systems (4)
Introduces electronic trading systems. Uses the R programming language to develop, evaluate, and optimize quantitative trading strategies. Students apply trading strategies through a live paper-trading account with an online broker using real time market data.

CFRM 523 Advanced Trading Systems (4)
Provides a detailed research process and tools for replicating, assessing, conceptualizing, and developing systematic trading strategies. Students apply their knowledge in projects to replicate and evaluate existing research and to create and evaluate a new strategy model. Prerequisite: CFRM 522.

CFRM 524 Advanced C++ for Finance (4)
Builds on CFRM 520 and covers modern algorithms, techniques, and libraries in C++ that enhance both computational performance and reliability in the implementation of quantitative financial models. Prerequisite: CFRM 520 or equivalent, or instructor permission.

CFRM 526 FinTech, Blockchains, and Cryptocurrencies (4)
Covers financial technology (FinTech) innovations and development, and the associated computational finance and risk management methods and perspectives. Includes real-world applications, including robo-advising, AI and Machine Learning for trading, etc. Also covers blockchain technology with focus on its applications to finance, especially cryptocurrencies. Prerequisite: CFRM 501; and CFRM 506 or CFRM 507, or equivalent, or instructor permission; recommended: ability to program and compile in R and/or Python.

CFRM 530 Fixed Income Analytics (4)
Covers fixed income markets and securities, data sources, analytics and portfolio management methods, in particular the valuation, risks, and risk management of fixed income securities. Uses a hands-on data-oriented and computational focus. Offered: A.

CFRM 531 Portfolio Performance Analysis and Benchmarking (4)
Covers fundamental principles and commonly used methods in performance measurement, analysis, and benchmarking of portfolio evaluation. Prerequisite: CFRM 501, MBA level investments course, or equivalent. Offered: A.

CFRM 532 Endowment and Institutional Investment Management (2)
Focuses on the endowment management process and specific challenges facing institutional fund managers. Includes evaluating the role of an endowment, portfolio construction, risk management, manager selection, and alternative asset class investing. Utilizes concepts from finance and investments, macroeconomics, and mathematical optimization. Prerequisite: CFRM 501. Offered: S.

CFRM 540 Risk in Financial Institutions (4)
Introduces the concepts and methodologies of financial risk management. Uses derivatives for hedging risk, emphasizing fixed income and exchange rate derivatives. Includes models, credit derivatives, mortgage backed securities, and asset backed securities. First in a sequence of three on financial risk management. Prerequisite: either CFRM 501 or permission of instructor. Offered: W.

CFRM 541 Quantitative Risk Management (4)
Provides a comprehensive treatment of the theoretical concepts and modeling techniques of quantitative risk management focusing on practical tools to solve real-work problems. Covers methods for market, credit, and operational risk modeling.

CFRM 542 Credit Risk Management (4)
Theory, applications & computational methods for credit risk measurement & management. Statistical and mathematical modeling of credit risk, emphasizing numerical methods & R programming. Methods include logistic regression, Monte Carlo simulation, & portfolio cash flow modeling. Covers default risk regression, analytics, & portfolio models of credit risk. Offered: A.

CFRM 550 Stochastic Calculus for Quantitative Finance (4)
Provides a systematic examination of financial derivatives pricing using stochastic calculus. Examines popular stochastic differential equation models such as Geometric Brownian motion, Vasicek, Hull-White, Cox-Ingersoll-Ross, Black-Karasinski, Heath-Jarrow-Morton, and Brace-Gatarek-Musiela, as well as Poisson and Levy processes. Applications include equity, fixed-income, and credit derivatives. Prerequisite: CFRM 504.

CFRM 580 Energy Markets Analytics and Derivatives (4)
Practices of valuation and risk management applied to energy portfolios. Covers valuation and risk methodologies applied to power, gas, and oil portfolios and discusses different market and credit risk metrics most relevant to energy market portfolios.

CFRM 586 Financial Time Series Forecasting Methods (4)
Covers financial time series forecasting methods and their use in making investment decisions for asset management purposes. Asset-class specific forecasting methods. Uses the R statistical modeling and data analysis system for implementing and evaluating such forecasting methods. Prerequisite: CFRM 501 or permissions of instructor. Offered: W.

CFRM 590 Special Topics (1-5, max. 15)
Topics of current interest in computational finance not covered by other graduate courses.

CFRM 600 Independent Research or Study (1-6, max. 18)

CFRM 601 Internship (1-6, max. 30)

CFRM 700 Master's Thesis (1-6, max. 18)