Learning Goals

Master of Science in Mathematical Finance

The Master of Science in Mathematical Finance program focuses on the distinct integration of certain practical domains of mathematics with an in-depth study of the theory and practice of modern finance. The program takes the perspective that in the field of mathematical finance (MF) “mathematical” modifies “finance” and that understanding the nature of the Brownian motion, however instrumental, does not amount to an understanding of finance.  The MF program focuses on the unique field of mathematical finance rather than treating mathematics and finance as separate entities.

Because of the distinctly integrated structure of the MF program, the program learning goals focus on the interplay between Mathematics and Finance.  Students master learning goals not through the content of a single course, but through the interplay of content across a carefully structured and sequenced curriculum.

MSMF Learning Goals

The learning goals of the MSMF program have been adopted by the MSMF Program Development Committee as the competencies all graduates of the program should attain.

Specifically, the learning goals of Boston University’s MSMF program are as follows:

  • We develop graduates who understand Financial Theory, including time value of money, risk preferences, market completeness, the principles of asset pricing, Arrow-Debreu securities and risk-neutral asset valuation
  • We develop graduates who understand Core Financial Instruments, Products, and Market Structures, including financial contracts and products, securities, options and futures exchanges, credit derivatives, financial institutions and financial regulations
  • We develop graduates who understand Financial Risk Management, including risk measures, debt instruments, credit and credit risk, and derivatives
  • We develop graduates who understand Relevant Mathematical Methods, including in-depth knowledge of core mathematical methods for building financial models
  • We develop graduates who understand Relevant Statistical Methods, including in-depth knowledge of the core statistical tools needed for calibrating financial models
  • We develop graduates who understand Relevant Computing Methods, including in-depth knowledge of the core numerical algorithms and computer programming tools that are widely used for solving financial models