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 educators teach advanced skills that can be used in the majority of industries. Graduates are prepared for sought-after positions in securities, banking, and financial management, and can also apply their skills at general manufacturing and service firms as 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, collaborative, peer-to-peer learning environment.
The two-year program is composed of nine courses as well as a Capstone project and examination. Each course is sequentially taught and builds on the previous one. 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 has seven week-long modules, followed by a one-week break before the next course starts. The course descriptions are provided below.
The Financial Markets course serves as an introduction to the field of Financial Engineering. It covers foundational topics such as the history of Financial Markets and Insurance; Market Regulation; Money Markets; Bond Markets and Trading, among others. The aim of the course is to expand students’ understanding of financial markets, analysis of market events and ability to perform valuations of financial instruments. Additionally, the course will incorporate discussions on recent developments such as High Frequency Trading and the Dodd-Frank Act.
The Econometrics course considers econometrics as statistical methods applied to finance. In this course, students apply statistical techniques to the analysis of econometric data. It starts with an introduction to the R statistical programming language, and students then use it to build econometric models, including multiple linear regression models, time series models and stochastic volatility models. By the end of this course students should be able to write programs using the R language, solve statistical problems and understand value distributions in modelling extreme portfolio returns, etc. The course concludes with modules on extreme value theory and risk management.
This course introduces derivative pricing in discrete time. It begins with measure-theoretic probability and stochastic processes, with an emphasis on discrete-time martingales. The course also covers modules such as Trading in Discrete-time, The Binomial Model, American Options and Interest Rate Models. These ideas are then applied to the pricing of derivatives in discrete time, followed by an introduction to interest rate and credit risk modelling. By the end of the course students will have an enhanced understanding of Discrete-time Stochastic Processes, including the language of measure-theoretic probability, define trading strategies in discrete time and create replicating portfolios.
The Continuous-time Stochastic Processes course covers key stochastic processes such as Brownian motion, Stochastic Calculus, Risk Neutral Pricing and Feynman-Kac Theorem. These modules would expand student’s knowledge around quadratic variations and proving martingale property, derive and prove Ito-Doeblin, understand first and second fundamental theories of finance and use the link between the RNPF to derive various PDEs. It will also cover PDE Solutions for Option Pricing such as Heston, SABR and Stochastic Volatility as well as Jump and Hawkes Processes.
This course provides a comprehensive introduction into computational finance, with a key focus on Monte Carlo methods in Python, Option Pricing, as well as 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, as well as an overview of Pricing Interest Rate Options such as HJM, SABR and LIBOR.
In the Portfolio Theory and Asset Pricing course, students will be introduced to single-period asset pricing, including the MVP theory, CAPM, SML and CML. The course will also cover multi-period asset pricing (Multi-period portfolio theory, CAPM and APT), Active frontiers, Bayesian Portfolio Theory and Indexation. Students will further be introduced to Stochastic Dynamic Control, where they will be required to understand and solve HJB equations. Closer to the end of the course students will be exposed to Transaction Costs, Incentives, Trading and Market Frictions.
The Machine Learning course covers the basic concepts of machine learning in finance. Students will learn about principles and applications of statistical learning, machine learning and tools therein. They will examine feasibility of learning, measures of fit and lift, and a handful of learning paradigms like logistic regression, neural networks, support vector machines, boosting, decision trees and more. Students will also be exposed to the development of supervised and unsupervised learning.
The Risk Management course is an introductory risk management course that seeks to present a comprehensive overview of risk management. It does so by using case studies of historical financial crises to expound on the need for risk management in the modern business environment. These case studies further highlight the major risks faced by businesses that include credit, market, operational, strategic, reputation and enterprise wide management risk. Finally, it covers the ethics and regulations surrounding risk management.
In the Data Feeds and Technology course, case studies are used as a method of understanding and analysing various data sets. The course begins with an introduction to Python for Data Science, which considers simulating financial data in Python. It also covers Excel/VBA for finance, Bloomberg Pro, B-Pipe and BLPAPI for finance, as well as charting and technical analysis. Additionally, overviews and case studies of Thomson-Reuters, TRTH and Eikon, DataStream and Distributed Ledger Technologies will be explored. Finally, the students will be 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 will practically apply their knowledge through a capstone project that they will have to plan, develop, document and defend. Through this process, students will have the opportunity to develop a Proof of Concept, identify and establish the technology management plan, manage project milestones, as well as write up and present the project. The aim of the capstone project course is to ensure that students have met the relevant learning outcomes of the program and are able to apply their learnings to real world situations.
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 pursue professional roles such as quantitative researchers, quantitative developers, quantitative traders, algorithmic traders, and portfolio managers for financial institutions.
Some focus on public policy, working for governments developing state and federal financial policies, or conducting research at think tanks.
There is a tremendous amount of fluidity between different financial-engineering careers, as well as transferable skills that allow professionals to easily move between these opportunities.
We believe location shouldn’t be a barrier to education. We use a web platform so students can complete their entire degree online from anywhere in the world, at any hour of the day.
From Singapore to Nigeria, our student community collaborates with peers and educators with diverse backgrounds from around the world.
Wherever you are, join a community at the forefront of financial engineering.
Our students are career-driven, computer-savvy quantitative thinkers. They have fully completed a bachelor’s degree or an equivalent 4-year degree and are interested in a future in financial engineering.
They come from a wide range of countries and have diverse backgrounds. They want to advance their career and seek life-changing education.
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.
Detailed information about WorldQuant University, the program, requirements for admission, academic policies, and other considerations are available in the WorldQuant University Catalog.
You can view and download a copy here.
The program will be offered quarterly, so there are four start dates every year.
|Start Date||Application Deadline|
|January 2Jan 2||Closed|
|April 2Apr 2||Closed|
|July 3Jul 3||Closed|
|October 2Oct 2||September 18Sep 18|
|Start Date||Application Deadline|
|January 8Jan 8||December 25Dec 25|
|April 2Apr 2||March 19Mar 19|
|July 2Jul 2||June 18Jun 18|
|October 1Oct 1||September 17Sep 17|