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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.


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.

The curriculum, with comprehensive coverage of financial markets and an emphasis on linking theory with real world developments, 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.

The Program is continuously enhanced with relevant and up-to-date courses in quantitative finance. The curriculum also encourages students to seek internships suiting their interests and to take advantage of the program’s network in the job search process.

Recently, new courses in algorithm trading, market microstructure, financial computing, structuring and trading strategies, and financial infrastructure, among others, have been offered by faculty members who are actively engaged in financial mathematics research and seasoned finance professionals with advanced degrees from leading institutions of higher learning.

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.



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A great benefit of doing the independent research is getting to know what doing research actually means in financial mathematics. That was a meaningful learning experience, which has certainly helped to shape my theoretical background further and prepare for my PhD study.
Mao, Hongyu
2016
PhD Candidate, HKUST