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• 金融衍生品：John Hull’s Options, Futures, and Other Derivatives. The so-called Bible of Wall Street Professionals, this book is mandatory reading for everyone entering the mathematical finance field. Somewhat dry at times, but the topics covered, presentation, and relevance to the program has no equal.
• 金融工程：Saleh Neftci’s Principles of Financial Engineering. A great synopsis of the interaction between financial instruments and asset classes within the markets. The late Professor Neftci was truly a gifted writer.
• 金融书籍的随机微积分Steven Shreve’s Stochastic Calculus for Finance books: namely Stochastic Calculus for Finance I: The Binomial Asset Pricing Model and Stochastic Calculus for Finance II: Continuous-Time Models. These books are standards for courses in stochastic calculus; but caution, these books can be hard to read the first time through, especially the Continuous-Time Models.
• 公司金融：推荐Stephen A. Ross, Corporate Finance。
• 计量经济学：推荐Jeffrey M. Wooldridge, Introductory Econometric
• Ross: “A First Course in Probability”
• Mood, Graybill & Boes: “Introduction to the Theory of Statistics”
• Rudin: “Principles of Mathematical Analysis”
• R语言：Paul Teetor’s R Cookbook. A great, simple-to-read-and-do tutorial on the R scripting language and R framework. Many courses will rely on R or some statistical-based package. Being proficient in R will be a great time-saver as well as tool that will be useful for all time.
• 金融工程和计算：Yuh-Dauh Lyuu’s Financial Engineering and Computation. A great book that touches mainly on the computational aspects of mathematical finance.
• 投资学--博迪
• 华尔街见闻
• 雪球财经网
波士顿大学金融数学项目推荐自学书籍和材料
The key to success is to ensure that you are well prepared for the rigorous course material that lies ahead in the MSMF program. The following recommendations should guide your preparation.
MSMF 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:
FINANCE
· 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.
ECONOMETRICS
· 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.
OPTIMIZATION
· Osborne, M.J. Mathematical Methods for Economic Theory. (https://mjo.osborne.economics.utoronto.ca/index.php/tutorial/index/1/toc).
R PROGRAMMING
Download R here: R project web site and here: https://www.rstudio.com.
· 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)
PYTHON PROGRAMMING
Download Python (Anaconda distribution) here: https://www.anaconda.com/distribution/
· Think Python (https://en.wikibooks.org/wiki/Think_Python)
· Hilpisch, Y. Python for Finance, O’Reilly, 2014.
· Hilpisch, Y. Derivatives Analytics with Python, Wiley, 2015
· Sargent, T. and J. Stachurski. Quantitative Economics with Python. (https://lectures.quantecon.org/py/)
