Please consult the Directory of Classes for authoritative up-to-date information for Spring 2021 courses when it becomes available closer to the end of Fall 2020.. Information like this changes frequently, and the present page is not necessarily up to date.

### Spring 2021 Mandatory MAFN Courses

**Math GR5030 Numerical Methods In Finance
**Directory of Classes

Vergil

Time: Monday and Wednesday 7:40pm-8:55pm

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructor: Tat Sang Fung

**Math GR5050 Practitioners’ Seminar**

Directory of Classes

Vergil

Time: Tuesday and Thursday 7:40pm-8:55pm

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructor: Lars Tyge Nielsen

MAFN Students ONLY

**Stat GR5265 Stochastic Methods in Finance
**MAFN students should NOT register for the other version of this course, Stat GU 4265

Two alternative sections:

**S****e****ction 001
**Directory of Classes

Vergil

Time: Monday and Wednesday 4:10pm-5:25pm

Location: TBA

Call Number: TBA Points: 3

Instructor: Ruimeng Hu

**Section 002
**Directory of Classes

Vergil

Time: Tuesday and Thursday 6:10pm-7:25

Location: TBA

Call Number: TBA Points: 3

Instructor: TBA

The following core course is not for current first-year full time MAFN students, who have already taken it in the Fall. It is a Fall core course taught off-schedule. It may be of interest to continuing part-time students.

**Math GR5010 Introduction to the Mathematics of Finance
**Directory of Classes

**Vergil**

Time: Monday and Wednesday 7:40pm-8:55pm

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructor: Mikhail Smirnov

### MAFN Electives

In the Spring semester of 2021, the MAFN program offers the following electives.

These courses are not mandatory. MAFN students may choose their electives from across the university, subject to the constraints of the MAFN degree requirements and the constraints imposed by the schools and departments offering the courses.

See Elective Course Examples for inspiration.

**Math GR5260 Programming for Quantitative & Computational Finance
**Directory of Classes

Vergil

Time: Friday 8:10pm-10:00pm

Location: TBA

Call Number: TBA Points: 3

Instructor: Ka Yi Ng

This course covers programming with applications to finance. The applications may include such topics as yield curve building and calibration, short rate models, Libor market models, Monte Carlo simulation, valuation of financial instruments such as options, swaptions and variance swaps, and risk measurement and management, among others. Students will learn about the underlying theory, learn coding techniques, and get hands-on experience in implementing financial models and systems. The Spring version of this course uses Python.

**Math GR5360 Math Methods in Financial Price Analysis
**Directory of Classes

Vergil

Time: Saturday 2:00pm-4:20pm.

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructor: Alexei Chekhlov

This course covers modern statistical and physical methods of analysis and prediction of financial price data. Methods from statistics, physics and econometrics will be presented with the goal to create and analyze different quantitative investment models.

**Math GR5380 Multi-Asset Portfolio Management
**Directory of Classes

Vergil

Time: Monday and Wednesday 6:10pm-7:25pm.

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructors: Inna Okounkova, Colm O’Cinneide and Irina Bogacheva

The course will cover practical issues such as: how to select an investment universe and instruments, derive long term risk/return forecasts, create tactical models, construct and implement an efficient portfolio,to take into account constraints and transaction costs, measure and manage portfolio risk, and analyze the performance of the total portfolio.

**Math GR5400 Non-Linear Option Pricing
**Directory of Classes

Vergil

Time: Friday 6:00pm-8:10pm.

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructors: Julien Guyon and Bryan Liang

Prerequisites: We assume familiarity with Brownian motion, Itô’s formula, stochastic differential equations, and Black-Scholes option pricing.

Nonlinear Option Pricing is a major and popular theme of research today in quantitative finance, covering a wide variety of topics such as American option pricing, uncertain volatility, uncertain mortality, different rates for borrowing and lending, calibration of models to market smiles, credit valuation adjustment (CVA), transaction costs, illiquid markets, super-replication under delta and gamma constraints, etc.

The objective of this course is twofold: (1) introduce some nonlinear aspects of quantitative finance, and (2) present and compare various numerical methods for solving high-dimensional nonlinear problems arising in option pricing.

This course also exposes the students with a wide variety of Machine Learning techniques, old and new. These techniques allow us to compute some quantities that are key ingredients of the nonlinear Monte Carlo algorithms.

**Math GR 5430 Machine Learning for Finance**

Directory of Classes

Vergil

Time: TBA

Location: TBA

Call Number: TBA Points: 3

Instructor: Renzo Silva

The application of Machine Learning (ML) algorithms in the Financial industry is now commonplace, but still nascent in its potential. This course provides an overview of ML applications for finance use cases including trading, investment management, and consumer banking. Students will learn how to work with financial data and how to apply ML algorithms using the data. In addition to providing an overview of the most commonly used ML models, we will detail the regression, KNN, NLP, and time series deep learning ML models using desktop and cloud technologies. The course is taught in Python using Numpy, Pandas, scikit-learn and other libraries. Basic programming knowledge in any language is required.

**Math GR5510 MAFN Fieldwork
**Directory of Classes

Vergil

Time: N/A

Location: N/A

Call number: TBA Points: 1-3

Prerequisites: All 6 MAFN core courses and at least 6 credits of approved electives. As a consequence, this course is not open to students in their first two semesters

Instructor: Lars Tyge Nielsen

MAFN Students ONLY. Permission of instructor required. Grading: Letter Grade

This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree.

For course description, rules, and procedures, please see Fieldwork Course (CPT)

#### Suggested Elective from the Statistics Department for MAFN Students

**Stat GR5206 Statistical Computation and Intro Data Science
**Directory of Classes

Vergil

Time: Friday 10:10am-12:40pm.

Location: TBA

Section 001 Call Number: TBA Points: 3

Instructor: TBA

Print this pageIntroduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.