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选修科目

 

Course      Code

Course Title

Credit

Contact

Hours

Minimum-passing

Grade

MFE5150

Financial   Data Analysis(金融数据分析)

3

42

D


Brief course   description: This course studies   empirical research methods in financial   engineering, i.e., predictions   using data analysis and statistical inference,   and covers time series   models, GARCH models, regression, and other   statistical   methods.

MFE5160

Quantitative   Risk Management(定量风险管理)

3

42

D


Brief course   description: This course   provides students with an in-depth understanding of   financial risks by   building mathematical models in order to understand the   nature of   these risks and to manage them using relevant products and     instruments.  Topics include market risk such as interest rate risk,     credit risk, foreign exchange risk at trade level and portfolio level.   The   course also covers Value at Risk, liquidity risk and an   introduction to bank   regulations.

MFE5170

Term   Structure Models and Fixed-income   Securities

(利率期限模型和固定收益证券)

3

42

D


Brief course   description: The course provides   a systematic introduction to the   development, analysis and   implementation of interest rate models. The materials   will cover   linear interest rate products and yield curve construction;   vanilla   interest rate options and single rate option models, interest rate     exotics, the modeling of multi-rate term structure, and how to manage     interest rate exposure for fixed income portfolios.

MFE5180

Commodity   Markets and Modeling(大宗商品市场建模)

3

42

D


Brief course   description: This course   provides an introduction to economic, modeling,   pricing and risk   management issues in commodity markets. Topics include the     organization of commodity markets, stochastic modeling of commodity prices,     applications of commodity derivatives in risk management and the pricing of     commodity derivatives.

MFE5190

Credit Risk   Modeling and Products(信用风险模型和产品)

3

42

D


Brief course   description: The course   introduces credit risk modeling and credit   derivatives evaluation and   management. It covers structural models of default   risk,   intensity-based modeling, risk structure of interest rates, credit     default swaps, CDOs, and related products, default-dependent structure     modeling

MFE5200

Computational   Methods in Financial   Engineering(金融工程中的计算方法)

3

42

D


Brief course   description: The course   introduces the Monte Carlo method and its application   in asset pricing   and risk management. Topics include random number   generation, sample   paths simulation for stochastic processes, variance   reduction   techniques, SDE numerical solutions, price sensitivities     estimation, American option Monte Carlo pricing, and other related     applications in risk management.

MFE5210

Algorithm   Trading(算法交易)

3

42

D


Brief course   description: This course   introduces the foundations of securities trading and   discusses market   microstructure and optimal trading strategies.  It   covers the   nature of markets and prices, trading mechanism, market   microstructure   models, trading costs and optimal trading strategies, and high     frequency trading.

MFE5230

Special   Topics in Financial Engineering –   Corporate Risk Management

(金融工程专题公司风险管理)

3

42

D


Firms   continually face all kinds of   risk.  Corporate Risk Management (CRM) is   concerned with how the   firm can deal with such risk: via hedging (derivative   financial   instruments); financing and capital structure policies; insurance;   and   other strategies.  The course includes a number of case studies,     illustrating both good practice, and also some egregious errors.  By     “corporate” we mean that we emphasize non–financial firms.

MFE5240

Project (金融工程研究)

3

42

D


Brief course   description: This course is   designed to offer students opportunities to   perform financial   engineering related research under the supervision of   faculty members   of the programme.

MFE5250

Programming   for Financial Engineering(程序设计在金融工程中的应用)

3

42

D


Brief course   description: This course is   designed for those students who would like to   acquire introductory   knowledge in programming and who aim to be financial   engineers,   quantitative analysts, accountants, and other finance   professionals.   The course first gives an introduction to basic   object-oriented   programming skills in MATLAB and C++. Equipped with basics in   MATLAB   and C++, application programming interfaces (API) for popular     financial databases will be covered. Selected applications in pricing, risk   management   of financial instruments, and asset allocation models are   covered at the end   of the course.

MFE5260

Data Sciences     in Financial Engineering

(数据科学在金融工程中的应用)

3

42

D


Brief course   description: This course   introduces basic concepts, algorithms, techniques   required for data   analytics at massive level, aiming to developing   problem-solving   skills in a data-intensive market environment. It covers   topics   including massive data management, algorithms for data mining, and     basic statistical modelling.  Applications of data mining techniques in     financial engineering and portfolio management will be discussed with     hands-on practice on data mining packages.

MFE5270

Internship   Training 1(校外实习())



Pass


This course   links classroom knowledge and the   career path of the student with an actual   work setting   opportunity.  It provides students with hands-on work   experience   in the financial engineering related industry.  Students   should   have some ideas of their job interest, personal skill sets and career     path, and then secure a position in line with that.  Students should     take the initiative to contact the Career Master for assistance and   continual   feedback.  After the internship experience, students   can have a reflection   opportunity on the direction of their career   paths.

MFE5280

Internship   Training 2(校外实习())



Pass


This course   links classroom knowledge and the   career path of the student with an actual   work setting   opportunity.  It provides students with more hands-on work     experience in the financial engineering related industry.  Students     should have some ideas of their job interest, personal skill sets and   career   path, and then secure a position in line with that.    Students should   take the initiative to contact the Career Master for   assistance and continual   feedback.  This is a continuation of the   Internship Training 1 course,   allowing the students to have more depth   or breadth in their career paths.

MFE5290

Quantitative   Investment(数量化投资)

3

42

D


The industry   landscape of investment,   trading, and risk management has been revolutionized   by computing   technologies, data science, and financial engineering. To   progress in   tandem with the changes in the industry, the topics covered in   this   course include Alternative ETF Construction, Portfolio Theory, and     Empirical Finance. 

In addition   to mathematical modeling, an   important part of this course is the practical   aspect: computational   implementations with statistical tests. Given that   implementation and   test procedures are involved, this quantitative finance   course is   algorithmic and hands-on in nature.

MFE5300

Venture Capital & Entrepreneurial Finance(風險投資與企業金融)

3

42

D


This course examines emerging companies finance   under different angles: the investors’ viewpoint, the entrepreneurs’   viewpoint and the ecosystems in which they operate. Students will also   explore the different stages of emerging companies and the issues that they   face while evolving.

The course covers financial topics most relevant to   newly formed companies, with an emphasis on innovative startups that target   large markets and raise outside capital. It includes topics on: (1)   valuation, which is one of the course’s primary themes, underlying all of the   topics covered, (2) evaluating business opportunities, which focuses on the   underlying economic principles that differentiate large opportunities from   small opportunities, (3) funding business opportunities, which covers both   identifying a company’s needs and acquiring the capital to finance those   needs, and (4) discussing how successful entrepreneurial ventures “exit”.

MFE5310

Machine Learning and Its Applications in Finance(机器学习以及在金融中的应用)

3

42

D


This course is designed for those students who would   like to acquire introductory knowledge in machine learning and who aim to   apply machine learning theory in financial applications. The course first   gives an introduction to machine learning, and introduce the major tasks and   major models, e.g. SVM, Tree based models, reinforcement learning and deep   learning models. Selected applications such as financial news sentiment,   credit scoring, and reinforcement learning in trading decisions are covered   at the end of the course.