MSc in Financial Engineering

The Financial Markets course serves as an introduction to the field of Financial Engineering. It covers foundational topics including: The History of Financial Markets and Insurance; Market Regulation; Money Markets; and Bond Markets and Trading.

In this course, students apply statistical techniques to the analysis of econometric data, starting with an introduction to the R statistical programming languages that students will use to build econometric models including multiple linear regression models, time series models, and stochastic volatility models.

In this course, students will gain an enhanced comprehension of Discrete-time Stochastic Processes including: understanding the language of measure-theoretic probability, defining trading strategies in discrete time, and creating replicating portfolios.

This course covers key stochastic processes such as Brownian Motion, Stochastic Calculus including the Ito integral, the Black-Scholes Model, and Levy processes.

This course provides a comprehensive introduction to computational finance with a key focus on Monte Carlo Methods in Python, Option Pricing, and Risk Management.

In this course, students are introduced to principles and applications of statistical learning and machine learning, and will. During the course, students examine feasibility of learning, measures of fit and lift, and a number of learning paradigms such as logistic regression, neural networks, support vector machines, boosting, decision trees, and both supervised and unsupervised learning. At the end of the course students are also introduced to the latest trends in machine learning in finance.

The course introduces students to single-period asset pricing including the MVP theory, CAPM, SML and CML. The course also covers multi-period asset pricing (Multi-period portfolio theory, CAPM and APT), Active Frontiers, Bayesian Portfolio Theory and Indexation.

This course uses case studies of historical financial crises to expound on the need for risk management in the modern business environment. Each module highlights the major risks faced by business and society including credit, market, operational, strategic, reputation and enterprise-wide management risk.

In this course, case studies are used as a method of understanding and analyzing various data sets. The course begins with an introduction to R for Data Science, then explores C# for finance programs and concludes with a focus on distributed ledger technologies.

In the Capstone Course students practically apply their understanding of the program content by accomplishing project milestones from developing a problem statement, identifying the required technology to find a solution to the problem, submitting multiple drafts for peer review and instructor feedback, and finalizing and presenting their fully-developed project.