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Program Overview

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Financial mathematics, the application of mathematical solutions to problems in finance, is an amalgam of mathematics, statistics, finance theory and computer science. As the discipline brings efficiency and rigor to financial markets, instruments and the investment process, it has become increasingly important in regulatory concern.

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The Master of Science (MSc) program in Financial Mathematics focuses on preparing undergraduate students from quantitative disciplines, such as mathematics, statistics, and computing, to be professionals in contemporary finance and wealth management.

The program, launched in 2006 as “MSc in Mathematics (Financial Mathematics and Statistics)" and renamed in 2012 as  "MSc in Financial Mathematics", intended to place further emphasis on quantitative finance rather than distance itself from its statistical roots. In 2016, the program was extended from 12 months to 18 months (for full-time students and 3 years for part-time students), corresponding with more course requirements.

This rigorous program strives to offer relevant and practical courses in quantitative finance, such as topics relating to machine learning, financial markets and microstructure, structuring and trading strategies, while the well-designed curriculum prepares students for careers in quant and data science by providing project collaborations and networking opportunities. With the advent of financial computing, emerging topics in blockchain technology and artificial intelligence are also covered in the syllabus so as to promote fintech innovation and entrepreneurship. 

With comprehensive coverage of financial markets and an emphasis on linking theory with real world developments, the curriculum includes:

  • Mathematical, statistical and computational methods for security pricing, asset allocation, speculative trading, and risk management;
  • Valuable insight on the performance of various pricing models;
  • Option pricing theory, portfolio theory, risk models, time series analysis of financial data, financial economics, and computer programming;
  • Programming skills, data science techniques in statistics, machine learning and AI, and innovative financial technology.

Students will gain key employability skills, build an extensive career network, and develop an advanced understanding of the major theoretical and applied concepts in financial mathematics from a cross-disciplinary and contemporary perspective.

Program objectives

  • To nurture the next generation of financial mathematic professionals for increasingly sophisticated markets.
  • To create a link between the theoretical and realistic worlds of financial mathematics.
  • To prepare for employment and contribute to the long-term sustainability and ever-evolving nature of the financial industry.

Intended learning outcomes

On completion of the program, students are expected to have:

  • Comprehensive knowledge of financial products commonly traded in the markets and solid understanding of models of security pricing and hedging in equity, fixed-income, forex and credit markets.
  • Solid understanding of the principles and technologies for risk management and trading strategies.
  • The ability to construct quantitative models and use them for production through quantitative programming.

Program Structure

Students are required to complete 36 credits, including at least 27 credits from the List of Required Courses (please see details below), and up to 9 credits of free electives/MAFS 6100 Independent Project.

You may find a list of required courses and credit requirements in this Program & Course Catalog. Please note that there are no compulsory courses required for graduation so you can enjoy a wide range of flexibility in course selection.

Visit the ‘Admission’ page for more information about the application timeline and admission information. For more information regarding the academic background of the students, visit the ‘Admission Statistics (cohort of 2022)’ pie chart.

Career Opportunities

Designed for pursuing a professional degree in quantitative finance with courses covering mathematics, statistics, computing and financial markets, graduates from this program are well-equipped with critical reasoning, digital literacy skills, and advanced knowledge that allows them to effectively interpret and apply theories in a wide range of practical contexts.

The program also encourages students to seek internships suiting their interests and to take advantage of the program’s network in the job search process. 

Past graduates have embarked on careers in trading and pricing derivative financial securities, quantitative analysis, data science, auditing and model validation, risk management, IT development, or even pursued PhDs in the relevant fields.

For more information regarding career prospects as a MAFM graduate, see the ‘Full-time employment and internship (2021)’ pie chart and the ‘Placement Statistics’ page.

Zhou, Hongyu
Cohort of 2016
Previous Degree: BEc in Finance, Sun Yat-Sen University