INTRO TO THE MATH OF FINANCE
Prerequisites: MATH UN1102 and MATH UN1201 , or their equivalents. Introduction to mathematical methods in pricing of options, futures and other derivative securities, risk management, portfolio management and investment strategies with an emphasis of both theoretical and practical aspects. Topics include: Arithmetic and Geometric Brownian ,motion processes, Black-Scholes partial differential equation, Black-Scholes option pricing formula, Ornstein-Uhlenbeck processes, volatility models, risk models, value-at-risk and conditional value-at-risk, portfolio construction and optimization methods.
- MW 7:40 p.m.-8:55 p.m.
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Instructor:
Mikhail Smirnov
CULPA: 16 Info - 3 points
NUMERICAL METHODS IN FINANCE
Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Review of the basic numerical methods for partial differential equations, variational inequalities and free-boundary problems. Numerical methods for solving stochastic differential equations; random number generation, Monte Carlo techniques for evaluating path-integrals, numerical techniques for the valuation of American, path-dependent and barrier options.
- M 6:10 p.m.-8:40 p.m.
- 3 points
MATH FINANCE PRACTITIONERS SEMINAR I
MATH FINANCE PRACTITION S
This seminar offers participants the opportunity to listen to practitioners discuss a range of important topics in the financial industry. Topics may include portfolio optimization, exotic derivatives, high frequency analysis of data and numerical methods. While most talks require knowledge of mathematical methods in finance, some talks are accessible to a more general audience.
- W 6:10 p.m.-7:25 p.m.
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Instructor:
Jaehyuk Choi
Info - 1.5 points
QUANT MTHDS IN INVESTMENT MGMT
Prerequisites: Knowledge of statistics basics and programming skills in any programming language. Surveys the field of quantitative investment strategies from a buy side perspective, through the eyes of portfolio managers, analysts and investors. Financial modeling there often involves avoiding complexity in favor of simplicity and practical compromise. All necessary material scattered in finance, computer science and statistics is combined into a project-based curriculum, which give students hands-on experience to solve real world problems in portfolio management. Students will work with market and historical data to develop and test trading and risk management strategies. Programming projects are required to complete this course.
- R 7:40 p.m.-10 p.m.
- 3 points
CAPITAL MARKETS & INVESTMENTS
Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfilio optimization. Capitol Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.
- S 7 p.m.-9:20 p.m.
- 3 points
HEDGE FUNDS STRATEGIES & RISK
The hedge fund industry has continued to grow after the financial crisis, and hedge funds are increasingly important as an investable asset class for institutional investors as well as wealthy individuals. This course will cover hedge funds from the point of view of portfolio managers and investors. We will analyze a number of hedge fund trading strategies, including fixed income arbitrage, global macro, and various equities strategies, with a strong focus on quantitative strategies. We distinguish hedge fund managers from other asset managers, and discuss issues such as fees and incentives, liquidity, performance evaluation, and risk management. We also discuss career development in the hedge fund context.
- T 8:10 p.m.-10 p.m.
- 3 points
NON-LINEAR OPTION PRICING
Prerequisites: familiarity with Brownian motion, Itô's formula, stochastic differential equations, and Black-Scholes option pricing. Prerequisites: 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.
- F 6 p.m.-8:10 p.m.
- 3 points
MODEL & TRADE DERIVATIVES
Required Prerequisite: Math GR5010 Intro to the Math of Finance (or equivalent). Recommended Prerequisite: Stat GR5264 Stochastic Processes – Applications I (or equivalent).
The objective of this course is to introduce students, from a practitioner’s perspective with formal derivations, to the advanced modeling, pricing and risk management techniques of vanilla and exotic options that are traded on derivatives desks, which goes beyond the classical option pricing courses focusing solely on the theory. It also presents the opportunity to design, implement and backtest vol trading strategies. The course is divided in four parts: Advanced Volatility Modeling; Vanilla and Exotic Options: Structuring, Pricing and Hedging; FX/Rates Components: Discounting, Forward Projection, Quanto and Compo Options; Designing and Backtesting Vol Trading Strategies in Python.
- S 10:10 a.m.-noon
- 3 points
ADVANCED MACHINE LEARNING FOR FINANCE
ADV MACHINE LEARNING FOR
The application of Machine Learning (ML) algorithms in the Financial industry is now commonplace, but still nascent in its potential. This course prepares the next generation of researchers and practitioners for the coming revolution, providing an advanced "deep dive" into machine learning methods (both theory and application) that are deemed to be useful for financial applications, including trading and investment management.
- T 12:10 p.m.-2 p.m.
- 3 points
MAFN FIELDWORK
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructors permission. See the MAFN website for details. This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.
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Instructor:
Jaehyuk Choi
Info - 1-3 points
Career Development for Quantitative Fina
Career Develop for Quant
This course helps the students understand the job search process and develop the professional skills necessary for career advancement. The students will not only learn the best practices in all aspects of job-seeking but will also have a chance to practice their skills. Each class will be divided into two parts: a lecture and a workshop.
In addition, the students will get support from Teaching Assistants who will be available to guide and prepare the students for technical interviews.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Jaehyuk Choi
Info - 0 points
Topics in Mathematical Finance
The purpose of this course is for MA in Mathematics of Finance students to gain knowledge and practical skills that are essential in the finance industry. The course will run as a series of lectures and discussions on various relevant topics, such as business communications and career talks that may feature guest speakers from the industry as well as the full-time faculty members. This will prepare the students for their job search, networking, and in their industry jobs in the future.
- TR 6:10 p.m.-7:25 p.m.
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Instructor:
Jaehyuk Choi
Info - 0 points