Degree: Master of Science
Length: 2 years
This field is on the rise as financial innovation across the globe drives demand for analytics and data science training.
From evaluating statistics to econometric modeling, our students learn advanced skills that can be applied across industries. Graduates are well positioned for careers in securities, banking, and financial management as well as in general manufacturing and service firms that increasingly rely on the expertise of quantitative analysts.
“Hiring skilled students who come from institutions like WorldQuant University is a business imperative.”
Designed by industry experts, WorldQuant University’s program integrates mathematical, statistical, and computer science tools with finance theory in a completely online and collaborative setting. Graduates are positioned to excel in today’s highly collaborative, fast-paced, professional environments.
The two-year program consists of nine graduate-level courses as well as a Capstone project and examination. The courses are sequentially taught and build on one another. Taking one course at a time allows you to earn your degree without disrupting your life.
All courses are delivered online and focus on applied projects.
The MSc in Financial Engineering is comprised of nine courses as well as a Capstone project and examination. Each course consists of seven one-week-long modules with one-week breaks between courses.
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. The aim of the course is to expand students’ understanding of financial markets, enable them to complete an analysis of market events, and provide students with the skills to perform valuations of financial instruments. The course also incorporates discussions on High Frequency Trading and the Dodd-Frank Act.
In this course, students apply statistical techniques to the analysis of econometric data. The course starts with an introduction to the R and Python statistical programming languages that students will use to build econometric models including multiple linear regression models, time series models, and stochastic volatility models. Students learn to develop programs using the R language, solve statistical problems, and understand value distributions in modelling extreme portfolio and basic algorithmic trading strategies. The course concludes with a review on applied econometrics in finance and algorithmic trading.
This course introduces derivative pricing in discrete time, beginning with measure-theoretic probability and stochastic processes with an emphasis on discrete-time martingales. The course continues by focusing on concepts of Trading in Discrete-time, The Binomial Model, American Options, and Interest Rate Models. These concepts are then applied to the pricing of derivatives in discrete time as a prelude to discussions on interest rate and credit risk modeling. By the end of the course, students will have 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. The course expands the student knowledge on quadratic variations, proving martingale property, deriving and proving Ito-Doeblin, and understanding the first and second fundamental theorems of finance. In the last module of the course, some of the most important interest rate models are addressed in detail.
This course provides a comprehensive introduction to computational finance with a key focus on Monte Carlo Methods in Python, Option Pricing, and Risk Management. The Monte Carlo Methods for Options Pricing considers the Pricing of American and Exotic options, whereas the Monte Carlo Methods for risk management considers CVar and Var Simulations. The course also delves into Fourier and Local Volatility for option pricing and offers an overview of Pricing Interest Rate Options such as HJM, SABR and LIBOR.
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. Students are introduced to Stochastic Dynamic Control, which they will use to understand and solve HJB equations. Transaction Costs, Incentives, Trading and Market Frictions are also addressed at the end of the course.
This course covers the basic concepts of machine learning in finance. Students are introduced to principles and applications of statistical learning and machine learning. 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.
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. Drawing on actual data, students perform analyses and apply the methods and processes they have learned in previous courses. At the end of the course, students are given an opportunity to consolidate their knowledge by reflecting on and evaluating the ethics and regulations associated with risk management.
In this course, case studies are used as a method of understanding and analyzing various data sets. The course begins with an introduction to Python for Data Science, which considers simulating financial data in Python. The course continues with a discussion on Excel/VBA for finance, Bloomberg Pro, B-Pipe and BLPAPI for finance, and charting and technical analysis. Overviews and case studies of Thomson-Reuters, TRTH and Eikon, DataStream, and Distributed Ledger Technologies are explored as well. At the end of the course, the students are introduced to Complex Event Processing as a means of understanding Esper and Java with Eclipse for CEP.
The Capstone course is designed to put the students’ knowledge of financial engineering to the test. Students practically apply their understanding of the program content by first developing a Proof of Concept, followed by identifying and establishing a technology management plan, managing project milestones, and writing up and presenting their project. The aim of the Capstone Course is to ensure that students have met the relevant learning outcomes of the program and are able to apply their knowledge and skills to real-world scenarios.
The Capstone Examination is an opportunity for students to provide a theoretical account of their learnings from the program. Students will have a limited amount of time to complete the exam and will have to prove their understanding of key theoretical components covered in the program.
Financial engineers typically pursue professional careers in quantitative research, quantitative development, quantitative trading, algorithmic trading, and portfolio management for financial institutions.
Others may choose to apply their skills to public policy work, conducting research, testing hypotheses, and developing financial policies for governments and think tanks.
From healthcare and drug discovery to supply chain optimization and security, there’s a growing demand across industries for financial engineers. Learning highly transferable skills makes it possible to easily move between opportunities.
We believe location should not be a barrier to education. Students use our web platform to complete their entire degree online from anywhere in the world, at any hour of the day.
Wherever you are, join a community of diverse students and educators at the forefront of financial engineering.
Our students are career-driven, computer-savvy quantitative thinkers. They have fully completed a bachelor’s degree and are interested in a future in financial engineering.
Students come from a wide range of countries and have diverse backgrounds. They want to advance their career and seek life-changing education. They are persistent, resilient, and committed to meeting the demands of our rigorous program and to mastering advanced concepts. They understand the value of collaborative work and value sharing knowledge as much as acquiring it.
Students are expected to commit 25 hours per week between lecture videos, assignments, group projects, and individual study.
WorldQuant University weighs several factors in evaluating applicants. Academic records are prioritized, but we also consider professional work experience, professional references, civic leadership, and extracurricular activities.
All students at WorldQuant University need access to a computer and a high-speed internet connection in order to participate in the online courses. Detailed information about WorldQuant University, the program, requirements for admission, academic policies, and other considerations are available in the WorldQuant University Catalog.
The program will be offered quarterly, so there are four start dates every year.
|Start Date||Apl. Deadline|
|April 2Apr 2||March 19Mar 19|
|July 2Jul 2||June 18Jun 18|
|October 1Oct 1||September 17Sep 17|