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吕安琪美国部后期组长

美国金融工程专业申请要求2
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3. 先修课

美国的金融工程硕士看重学生在数学,计算机和金融方面的知识和能力储备。尤其是数学和计算机能力出众的学生,往往录取结果更好。总的来看,常见的先修课主要有以下:

3.1. 数学和统计

·         微积分 (Differential Calculus, Multivariate Calculus)

·         线性代数(Linear Algebra)

·         概率论和统计(Probability and Statistics)

·         微分方程Differential Equations(偏微分PDE&常微分ODE)

以上为最基础的要求,但是要想有更大优势,尽可能多修

·         随机过程Stochastic Processes

·         数值分析Numerical Analysis

·         计量经济学Econometrics

·         时间序列Time Series

·         实变函数Real Analysis

·         优化Optimization

 

3.2. 计算机

·         C, C++, Matlab, Python, R (目前最常见的)

·         Machine Learning

·         SAS, Gauss, RATS, S-Plus, or Garch

  

3.3. 金融

          微观经济学Microeconomics

          宏观经济学 Macroeconomics

          公司财务及财务分析 Corporate Finance and Financial Analysis

          货币和资本市场 Money and Capital Markets

          投资学 Investments


修课方式:

先修课尽量在学校内修课,并获得成绩单。如果校内没有条件修课,可以通过以下方式:


A:http://www.coursera.org/

For mathematics, the following are suggested courses:
Probability Theory
Statistics and Inference
Linear Algebra
Linear Optimization

For computer programming, the following are suggested courses:
R Programming
The Data Scientist’s Toolbox

For those students who want to get a head start on mathematical finance:
Mathematical Finance
Financial Engineering and Risk Management Part 1
Financial Engineering and Risk Management Part 2

 

B:https://quantnet.com/courses/ (C++)价格高,难度大,通过率只有40%,优秀率只有7%。由Baruch MFE 教授授课,修过此课可以不用参加CMU mscf的暑假提前课的。


C:自学推荐书籍&新闻杂志:

       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: namelyStochastic 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

 

D.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

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