金融工程-新东方前途出国

 

1. 专业简介

Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance. --Definition from Wikipedia

金融工程概念：

·         利用数学工具、工程化手段来解决实际金融问题：

·         金融产品设计，金融产品定价，交易策略设计，金融风险管理等；

第一个金融工程学位课程是在20世纪90年代初设立的。这个专业的数量和规模都在迅速增长。目前美国USNEWS排名150的院校中，有50左右个金融工程硕士项目。

 

关于金融工程更多内容可以参考国际量化金融协会网站https://www.iaqf.org/

 

2. 专业分支

美国院校常见的专业名称：

u Financial Engineering  金融工程

u Financial Mathematics /Mathematical Finance  金融数学

u Computational Finance 计算机金融

u Quantitative Finance  量化金融

u Quantitative Finance and Risk Management  量化金融和风险管理

 

3. 所属学院

         工学院：

偏向招收工程专业学生，重视编程能力，会开设optimization , programming等课程。

(Columbia MSFE, Stanford MSFM, Cornell MSFE, USC MSFE, NYU MSFE

商学院：

 

偏向招收金融专业学生，围绕金融方面学习，会开设Stochastic Methods of Mathematical Finance等课程。

(WUSTL MSF,  UCB MFE, UCLA MFE, Gatech MSQ&CF, BU MSMF,  SIT MSFE,  SUNY-Buffalo MSF,  Temple MSFE, IIT MMF)

 

数学学院：

偏向招收数学专业的学生，课程围绕数学、统计展开，会开设Stochastic Processes等课程，学术性较强。

 (Columbia MAMF, Chicago MSFM, JHU MSFM, UND, USC MSMF, NYU MSMF, UW MSCF, Purdue MSCF, SUNJ MSMF, Minnesota MSFM, SUNY- Stony Brook MSQF, FSU MSFM, Dayton MSFM)

 

1. 专业背景

总体看来，除了拥有金融、数学、经济、统计、经济计量背景的人，其它方向如计算机、物理、化学、工程等背景的人同样是很受欢迎的申请者。而且在这些"转专业"的人中，工程类专业背景的学生占了将近半数。 如果是纯商科背景， 比较偏好有辅修或者第二专业为相关理工科。

 

2. 成绩建议

·         托福

1）基本要求：总分100，每项不低于23

2）有1%-3%加分，总分110分，口语25分

   

·         GRE

1）GRE基本要求：153+165+3.0

2）有1%-3%加分，160+170+3.5

 

·         GPA

1）大于3.7，有竞争力的成绩

2）3.5-3.7，无优势无劣势

3）3.5以下，无优势成绩

 

 

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: 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

 

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, 5^th 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, 3^rd 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/)

 

4. 面试

1）Technical面试（知识层面）：

•     （quant类）基础微积分、线性代数、概率统计、brain teaser、基础编程、金融衍生品、随机微积分

•     （市场类）投资机会、对中国市场的看法、未来经济状况等等  

2）Behavior 面试

•       Why America？Why finance？Why MFE？

•       Linked ln查询面试官信息：术界or业界？什么领域

•       thank you letter

 

5. 推荐实习

·         投资银行——大的title相对于普通中国证券公司会更有帮助，即使是做IBD、IT

·         证券公司——一般研究部下有专门的金融工程组/金融衍生品部/金融创新部等与金融衍生品相关部门；其他可以考虑的有：固定收益部/资产管理部/量化投资部/风险管理部/产品研发部

·         商业银行（工商银行、中国银行、建设银行等）总行一般设有风险管理部门，会涉及信用风险以及市场风险模型的建立方面的工作

·         基金公司/期货公司：量化投资/风险管理/金融股指期货方面的研究与建模

·         IT公司/咨询公司：数据分析相关实习，如运用数据挖掘方法进行消费者行为的研究或者客户信息的管理；运用计量模型与统计软件进行深入的市场调研等等