The financial engineering courses are designed typically for people to pursue professional careers in quantitative research, quantitative development, quantitative trading, algorithmic trading, and portfolio management for financial institutions. Others courses to apply your skills to include 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 is a growing demand across industries for financial engineers. Learning highly transferable skills makes it possible to easily move between opportunities. Demands for financial engineers is on the rise as financial innovation across the globe drives demand for analytics and data science training.
This ranges from evaluation of statistics to econometric modelling, WorldQuant University students learn advanced skills that can be applied across industries. The financial engineering graduates will be 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.
The financial engineering courses Descriptions
The MSc in Financial Engineering is comprised of nine graduate-level courses as well as a Capstone course. Each of the nine courses consists of seven one-week-long modules with one-week breaks between courses. The final Capstone Course during which students complete a culminating Capstone project is 10 weeks long.
MScFE 560: Financial Markets
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
- 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.
MScFE 610: Econometrics
The course starts 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. 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.
MScFE 620: Discrete-time Stochastic Processes
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, and pricing and hedging both European and American Options.
These concepts are then applied to the pricing of derivatives in discrete time as a prelude to discussions on the interest rate and credit risk modelling. 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.
MScFE 622: Continuous-time Stochastic Processes
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.
MScFE 630: Computational Finance
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 transforms and Local Volatility for option pricing and offers an overview of Pricing Interest Rate Options such as HJM, SABR and LIBOR.
MScFE 640: Portfolio Theory and Asset Pricing
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.
MScFE 650: Machine Learning in Finance
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 the 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.
MScFE 660: Case Studies in Risk Management
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.
MScFE 670: Data Feeds and Technology
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, building on the Econometrics and Computational Finance courses. Following this, it explores C# for finance programs, before incorporating this with Excel for sophisticated financial data management and simulation.
The course also covers distributed ledger technologies, with particular attention to blockchains and their application in cryptocurrencies and smart contracts. At the end of this course, students will be empowered to engage in distributed ledger powered trading and will have the knowledge to launch their own trading tokens.
MScFE 690: Capstone Course
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 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. The goal of the Capstone Course is to ensure that students have met the program outcomes and are able to apply their knowledge and skills to real-world scenarios.
Who should apply for the Financial Engineering Course
WorldQuant University 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. The 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 also consider professional work experience, professional references, civic leadership, and extracurricular activities.
- Completed online application with all required documents
- Official transcripts from highest college/university degree earned. Bachelor’s degree (required)
- Passing score of 75% on the Quantitative Proficiency Test
- Proof of English proficiency (TOEFL, IELTS, or PTE scores).
As part of your application, you may submit a scanned copy of your transcript for the highest college or university degree you have earned. Upon acceptance into the program, you are required to submit official transcripts, which are sent directly from your former college or university to WQU via postal mail or email.
The Test of English as a Foreign Language (TOEFL), the International English Language Testing System (IELTS), or the Pearson Test of English (PTE) is required of applicants whose native language is not English. The only exception is for applicants who have earned a degree at an institution where the language of instruction is English.
Financial Engineering Application Process
Create an account
Once you have created an account, you will receive an automated email with a verification link. Clicking on the link will verify your account and allow you to fill out your profile and to upload the required documents.
Please be aware, you must complete your profile and upload your documents in one sitting. Before starting your application, please review the admission requirements and make sure to gather all the required documents.
Review admission requirements and make sure you are aware of deadlines
While every effort is made to accommodate students, submitting an application by the deadline does not guarantee acceptance or a seat in the desired course session. Admission to the Master of Science in Financial Engineering program is available to all qualified applicants who meet the above requirements.
Gather and upload your required documents
- Government Issued Photo ID. (e.g. national ID card, passport, or driver’s license)
- College/University Transcripts. (Bachelor’s Degree required)
- Proof of English Proficiency (if applicable). (TOEFL, IELTS, or PTE scores)
Remember, you will have to complete your profile and upload your documents in one sitting. Admission decisions can only be made once all the required documents are received. Before submitting, please double check all parts of your application to make sure you have uploaded all of the required documents. You will not be able to make any changes after submission (unless you need to update your contact information).
Take the Quantitative Proficiency Test
Upon submission of the preliminary portion of your application, you may proceed to take the Quantitative Proficiency Test to determine your mathematical and statistical proficiency. It may take up to three hours and must be completed in one sitting, so make sure you have enough time before you begin. To be eligible for admission, you must earn a minimum passing score of 75%.
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