Masters of Engineering in MIE Certificate Program in Financial Engineering March 2, QFRM 2012 Roy H. Kwon University of Toronto
Masters of Engineering at MIE The Master of Engineering program prepares graduate engineers for advanced professional practice, by completing graduate-level coursework and an optional project.
Program Requirements The M.Eng. program requires successful completion of ten half-courses, or seven half-courses and an M.Eng. project. For a program of ten courses, six must be taken from MIE. For a program of seven courses plus a project, four courses must be taken from MIE
7 Courses plus M.Eng. project The remaining courses (four or three, respectively) can be taken from any other department in the Faculty of Applied Science and Engineering (including the ELITE courses), the Department of Computer Science (CSC), and the Rotman School of Management (RSM1300-level, RSM2000-level and up).
Other graduate degree programs MASc & Ph.D: research oriented, fully funded by sponsoring professor or agency, thesis is the main focal point and must be defended, academic oriented research
New Concentration in Financial Engineering Motivation There is a significant interest in a graduate degree in quantitative finance schools (e.g. Columbia, Princeton, Michigan, NYU, CMU, many others) Financial modeling is a key applied area of Operations Research / Industrial Engineering, and an active research area within MIE and elsewhere in the Faculty.
Certificate in Financial Engineering within an M.ENG. Tentative Start Fall 2012 Specialization in quantitative finance Major themes: (1) portfolio construction (2) derivatives pricing (3) risk management Techniques covered: optimization (e.g. linear, dynamic, quadratic, stochastic programming), stochastic processes, statistics
F.E. Certificate Requirements Must adhere to rules of the general M.ENG. requirements The following core courses are required: MIE1605H:Stochastic Processes APS1002H:Financial Engineering MIE16XXH:Financial Engineering II MGT2308H:Financial Risk Management Plus a 3-credit M.ENG. Project (industry related)
MIE 1605 Stochastic Processes A course on the fundamentals of stochastic processes and their application to mathematical models in operational research. Topics discussed will include a review of probability theory, Poisson processes, renewal processes, Markov chains and other advanced processes. Applications in finance, inventory, queuing, reliability, repair and maintenance, etc.
APS 1002H Financial Engineering Modern introduction to investment science Topics (text: Investment Science by Luenberger) Interest Rate Theory Fixed Income Securities, Term Structure of Interest Rates, Bond Portfolio Optimization Modern Portfolio Theory (mean-variance optimization, 2 and 1 Fund Theorems, CAPM, Factor Models, APT) Derivative securities (forwards, futures, options)
MIE 16XX Financial Engineering II New Course First half: Optimization in finance (linear,, stochastic programming : financial models, theory, and algorithms) Second half: Stochastic calculus (arithmetic and geometric Brownian motion, Ito s lemma, Black-Scholes world, contingent claims pricing, lattice and monte-carlo techniques)
MGT2308H Financial Risk Management This course covers ways in which financial institutions, corporations, and fund managers can quantify and manage risks. It covers the calculation and use of value at risk, ways of forecasting volatilities and correlations, the quantification of credit risk, and credit derivatives.
Electives (choose 3-4) ECO2411H: Financial Econometrics STA2503H: Applied Probability for Mathematical Finance MIE562H: Scheduling MIE566H: Decision Analysis MIE1606H: Queuing Theory MIE1620H: Linear Programming and Network Flows MIE1603H: Integer Programming MIE1607H: Stochastic Modeling and Optimization MIE1613H: Discrete Event Simulation MIE1615H: Stochastic Dynamic Programming MIE1619H: Constraint Programming and Local Search MIE1621H: Non-Linear Optimization
New elective (Dr. Oleksandr Romanko, Algorithmics-IBM) Course title: Computational simulation and optimization in finance and risk management Course description: The goal of this course is to familiarize students with computational quantitative techniques that are used in finance and risk management. Simulation and optimization are among the most important quantitative tools, which allow to model and optimize financial portfolios taking into account uncertainty in future asset values. A number of financial and risk management applications are described in detail. Matlab is used for illustrating the computations as well as for developing a software package during the course project. Practical aspects of risk modeling, which are used by industry practitioners, are emphasized.
3-Term Project Course Project that will consolidate the tools and skills learned Two types of projects: Industry related or academic
Other related Masters program at U of T MMF: Masters in Mathematical Finance MFin: Rotman Both expense and admission is directed at a narrow segment of potential students M.ENG. Certificate in FE will be more accessible, more engineering based, and much less expensive.