The key to success is to ensure that you are well prepared for the rigorous course material that lies ahead in the MSMFT program. The following recommendations should guide your preparation.
MSMFT students are required to complete a two-week Preparatory Mathematics and Statistics program before the start of regular classes. Read the following reference material before the Math Prep classes start:
- William, D. Weighing the Odds: A Course in Probability and Statistics, Cambridge University Press, 2001, chapters 1 – 5, 7, 9.
- Rudin, W. Principles of Mathematical Analysis, McGraw Hill, 1976, chapters 3 – 5, 7, 9.
- Friedman, S., A. Insel and L. Spence, Linear Algebra, 5th edition, Pearson, chapters 1 – 5.
In addition to this, you should prepare for the course work to follow the Math Prep program. The best preparation for you will depend on your exposure (so far) to finance, economics, econometrics and computer programming. Read selectively from the following sources to fill any gaps in your background:
- Back, K. A Course in Derivative Securities, Springer, 2005, chapters 1 – 5.
- McDonald, R. Derivatives Markets, 3rd ed., Pearson, 2013, chapters 5 – 12, 18 – 24.
- Kosowski, R. and S. Neftci. Principles of Financial Engineering, 3rd ed., Elsevier, chapters 1 – 13.
- Lyuu, Y. Financial Engineering and Computation, Cambridge, 2004, chapters 1 – 20, 31.
- Gujarati, D. and D. Porter. Basic Econometrics, 3rd ed., McGraw-Hill, chapters 1-12, 21
- Wasserman, L. All of Statistics: A Concise Course in Statistical Inference, Springer, chapters 1 – 7, 9, 13.
- Osborne, M.J. Mathematical Methods for Economic Theory. (https://mjo.osborne.economics.utoronto.ca/index.php/tutorial/index/1/toc).
- Teetor, P. R Cookbook, O’Reilly, 2011.
- Venables, W. and D. Smith. An Introduction to R. ( https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)
Download Python (Anaconda distribution) here: https://www.anaconda.com/distribution/