Core Courses
The descriptions below briefly outline the core courses.
22:390:611 - (3 credits)
Analysis of Fixed Income Securities
This course is a quantitative course on fixed income analysis. It begins with bond mathematics, and continues with yield curve modeling, interest-rate risk, tools for fixed income portfolio management, embedded option pricing, arbitrage strategies and ends with credit risk modeling. Students will be familiar with various types of bond instruments such as Convertible, Callable bonds, PIK, MBS, CMO, Senior-Subordinate bonds and CDS. In addition, students will be heavily exposed to the tools available in the Bloomberg system. Students will be able to see how practitioners use these tools in decision making. This course is fundamental to students who are serious in pursuing a career directly relating to quantitative modeling of fixed income products.
Prerequisites: 390:587 or 390:522; and 390:603.
26:220:507 - (3 credits)
Econometrics
The purpose of this course is to develop basic econometric estimation and hypothesis testing tools necessary to analyze and interpret the empirical relevance of financial and other economic data. This requires developing statistical methods for estimation of population parameters and testing hypotheses about them using a sample of data drawn from the population distribution, under various assumptions regarding the true population relationship between the observable economic variables. Focus will be on the theoretical foundations of econometric analysis and strategies for applying these basic econometric methods in empirical finance and economics research. Topics covered include estimation and hypothesis testing using the classic general linear regression model, combining sample and non-sample information, dummy variables, random coefficients, multicollinearity, and the basics of large sample theory, non-spherical disturbances, panel data, systems of equations, time-series, and their application.
22:390:604 - (3 credits)
Financial Institutions & Markets
Presents a detailed overview of the theory and institutional features of the U.S. financial system. Provides a comprehensive review of U.S. financial markets. Covers a survey of flow-of-funds data and U.S. financial markets and institutions, capital market theory, financial factors and economic activity, theory of the level and structure of interest rates.
Prerequisites: 223:581 or 223:521; 223:591 or 223:520; and 390:587 or 390:522
22:839:571 - (3 credits)
Financial Modeling I
This is a quantitatively-oriented financial economics course for the Master of Quantitative Finance (MQF) students. The course covers the basic concepts and analytical techniques of modern portfolio theory and asset pricing. Topics include Fisher separation, risk analysis using expected utility theory, mean-variance analysis, capital asset pricing model, arbitrage pricing theory, state preference theory, consumption-based asset pricing, market efficiency, and empirical tests of asset pricing models.
22:839:662 - (3 credits)
Financial Modeling II
This course covers continuous time finance, similar to an advanced Ph.D. course in asset pricing. It follows Financial Modeling I which covers discrete time finance and continues with continuous time financial theories. Topic-wise, it covers basic theories (backward and forward equations, change of measure, state pricing, arbitrage pricing, martingales), derivatives pricing (Black-Scholes model, Heston model, Geske model, Merton-Rabinovitch model), term structure of interest rates (Vasicek model, CIR model, HJM model, Hull-White model), multi-factor models (Chen-Scott model, Bakshi-Cao-Chen-Scott model, Duffie-Pan-Singleton model), credit derivatives (Jarrow-Turnbull model, Duffie-Singleton model) and some numerical methods (binomial model, finite difference methods, Monte-Carlo). Interested students can get a good idea from the following books: Merton – Continuous Time Finance, Duffie - Dynamic Asset Pricing Theory, Ingersoll - Theory of Financial Decision Making, and similar others.
22:839:664 - (3 credits)
Fundamentals of Career Planning
Guided through a series of lectures, discussions, individual and group activities, role-plays, and assignments designed to educate, develop, and assist you to successfully navigate the challenging MQF profession; from self-discovery to job search to career management. Participation in information sessions, mock interviews, and coaching session is essential.
This course provides tools necessary for you to take ownership of your career and give you the competitive advantage critical to achieve your career goals. All 1st-year, full-time students are required to satisfactorily complete this course.
22:839:510 - (3 credits)
Numerical Analysis
This course derives, analyzes, and applies methods used to solve numerical problems with computers; solution of linear and nonlinear algebraic equations by iterations, linear equations and matrices, least squares, interpolation and approximation of functions, numerical differentiation and integration, and numerical solutions of ordinary differential equations.
22:839:614 - (3 credits)
Object Oriented Programming in Finance I
The goal of this year-long sequence of courses is to give a rigorous introduction to computer programming and software engineering with special emphasis on applications to financial engineering. Our primary programming language will be C++. This programming language is fast enough to accommodate the performance demanded in financial environments. At the same time C++ is an object oriented language and, as such, is suitable for modern software design. In this course the assumption is that students have had no background in computer programming, although even people who are familiar with some programming language will hopefully benefit and learn new material. In part I in the Fall semester the course will start with basic concepts of programming, but we quickly get into topics in object oriented programming, UML diagrams, and basic patterns. We will also include introduction to basic algorithms and data structures. In part II in the Spring semester, more advanced topics will be covered, including advanced algorithms and data structures especially through introduction to STL and boost libraries, numerical algorithms and introduction to BLAS and LAPACK libraries, design of graphical user interfaces, and concurrent programming (also known as multiprogramming).
22:839:615 - (3 credits)
Object Oriented Programming in Finance II
The goal of this year-long sequence of courses is to give a rigorous introduction to computer programming and software engineering with special emphasis on applications to financial engineering. Our primary programming language will be C++. This programming language is fast enough to accommodate the performance demanded in financial environments. At the same time C++ is an object oriented language and, as such, is suitable for modern software design. In this course the assumption is that students have had no background in computer programming, although even people who are familiar with some programming language will hopefully benefit and learn new material. In part I in the Fall semester the course will start with basic concepts of programming, but we quickly get into topics in object oriented programming, UML diagrams, and basic patterns. We will also include introduction to basic algorithms and data structures. In part II in the Spring semester, more advanced topics will be covered, including advanced algorithms and data structures especially through introduction to STL and boost libraries, numerical algorithms and introduction to BLAS and LAPACK libraries, design of graphical user interfaces, and concurrent programming (also known as multiprogramming).
22:390:609 - (3 credits)
Options
The purpose of this course is to provide students with the necessary knowledge on how to use and not to use the models for derivatives. While the course will primarily focus on payoffs, usage, pricing, hedging, and institutional details of the most fundamental or vanilla versions of Options and Futures, it will also deal in some detail with more recent studies in the theory of derivative pricing. Students will acquire a robust conceptual knowledge of the fundamental issues that determine the valuation and behavior of these instruments. Though this course focuses on the intuitive economic insights of those models, some advanced math is required, including stochastic calculus. Be prepared for some necessarily non-trivial math if you take the course.
26:711:563 - (3 credits)
Stochastic Calculus for Finance
The objective of the course is to provide the students with knowledge and skill sufficient for correct formulation and analysis of continuous-time stochastic models involving stochastic integrals and stochastic differential equations. Particular attention will be devoted to application of stochastic calculus methods in finance, such as models of evolution of stock prices and interest rates, pricing of options, and pricing of other contingent claims.The course will also prepare the students for independent research on problems involving stochastic calculus techniques.
