Please consult the Directory of Classes for authoritative up-to-date information for Spring 2022. Information like this changes frequently, and the present page is not necessarily up to date.

Please see the class schedule in a grid.

### Spring 2022 Mandatory MAFN Courses

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

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

Location: 614 Schermerhorn Hall

Section: 001

Call Number: 11912

Points: 3

Instructor: Tat Sang Fung

**Math GR5050 Practitioners’ Seminar**

Directory of Classes

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

Location: 207 Mathematics Building

Section: 001

Call Number: 11913

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

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

Location: 408 Zankel

Section: 001

Call Number: 13923

Points: 3

Instructor: Johannes Wiesel

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

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

Location: 207 Mathematics Building

Section: 001

Call Number: 11910

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**

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

Location: 312 Mathematics Building

Section: 001

Call Number: 11914

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

Days & Time: Saturday 7:00pm-9:20pm.

Location: 312 Mathematics Building

Section: 001

Call Number: 11916

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

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

Location: 207 Mathematics Building

Section: 001

Call Number: 11917

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

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

Location: 520 Mathematics Building

Section: 001

Call Number: 11919

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 GR5510 MAFN Fieldwork
**Directory of Classes

Days & Time: N/A

Location: N/A

Section: 001

Call number: 11921

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)

**Math GR 5430 Machine Learning for Finance
**Directory of Classes

Time: Saturday 2:40 p.m. – 4:30 p.m.

Location: 203 Mathematics Building

Call number: 18092 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.

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

**Note: **In order to register for these courses, MAFN students must get Instructor’s permission first by emailing her/him directly with the attached Registration Adjustment Form.

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

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

Location: 301 Pupin Laboratories

Section: 001

Call Number: 13908

Points: 3

Instructor: Wayne Lee

Introduction 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, parallelizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

**Stat GR5241 Statistical Machine Learning**

**Section 001
**Directory of Classes

Day & Time: Friday 6:10pm-8:55pm.

Location: 402 Chandler

Call Number: 13917

Points: 3

Instructor: Banu Baydil

**Section 002
**Directory of Classes

Day & Time: Friday 10:10am-12:40pm

Location: 402 Chandler

Call Number: 13918

Points: 3

Instructor: Xiaofei Shi

**Section 003
**Directory of Classes

Day & Time: Friday 2:40pm-5:25pm

Location: 402 Chandler

Call Number: 13920

Points: 3

Instructor: Kamiar Rahnama Rad

Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems – not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

**Stat GR6153 Probability II
**Directory of Classes

Day & Time: Tuesday and Thursday 4:10pm-5:25pm

Location: 507 Mathematics Building

Call Number: 11928

Points: 4.5

Instructor: Ioannis Karatzas

Prerequisites: MATH GR6151 MATH G4151 Analysis & Probability I. Elements of the general theory of random processes. Martingales, the Dood-Meyer, and optional decomposition theorems. Stochastic integration, the resulting Ito calculus, Girsanov change of measure, local time, integral equations, and diffusions. Entropy production for Markov processes, connections with optimal transport, gradient flows.

Stochastic theory of portfolios. Notions of deflators, numeraires, viability, and arbitrage. The growth-optimal portfolio and its properties. The basic problems of hedging, and of portfolio optimization, in finance

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