Program Overview

Mathematics in Finance Master of Science Program

The Mathematics in Finance Master of Science Program at NYU Courant is designed for individuals committed to launching careers in the financial industry. The program provides a rigorous academic exploration of mathematical finance and financial data science, preparing students to lead the financial industry into a better tomorrow.

Curriculum

The curriculum is one of a kind, with courses developed and designed to help students start their careers in the financial industry. The program includes a range of courses, such as:

• Computing in Finance: This course teaches students the popular Python programming language, object-oriented software development, and financial context projects.
• Data Science and Data-Driven Modeling: This half-semester course covers practical aspects of econometrics, statistics, and data science in an integrated and unified way as applied in the financial industry.
• Financial Securities and Markets: This course provides a quantitative introduction to financial securities for students aspiring to careers in the financial industry.
• Risk and Portfolio Management: This course introduces portfolio and risk management techniques for portfolios of equities, delta-1 securities, and futures, and basic fixed income securities.
• Stochastic Calculus: The goal of this half-semester course is for students to develop an understanding of the techniques of stochastic processes and stochastic calculus as applied in financial applications.
• Machine Learning and Computational Statistics: This course covers machine learning techniques, including supervised and unsupervised learning, and their applications in finance.
• Dynamic Asset Pricing: This advanced course covers asset pricing and trading of derivative securities using tools and techniques from stochastic calculus.
• Project and Presentation: Students conduct research projects individually or in small groups under the supervision of finance professionals, culminating in oral and written presentations of the research results.
• Scientific Computing: This course provides a practical introduction to computational problem-solving, covering topics such as numerical linear algebra, optimization, and Monte Carlo methods.
• Advanced Statistical Inference and Machine Learning: This course covers Bayesian statistics, multivariate regression, and machine learning techniques, with applications to finance.
• Alternative Data in Quantitative Finance: This half-semester elective course examines techniques dealing with the challenges of the alternative data ecosystem in quantitative and fundamental investment processes.
• Trends in Financial Data Science: This full-semester course covers recent and relevant topics in alternative data, machine learning, and data science relevant to financial modeling and quantitative finance.
• Fixed Income Derivatives: Models and Strategies in Practice: This course focuses on the practical workings of the fixed-income and rates-derivatives markets, covering bonds, swaps, flow options, and structured products.
• Time Series Analysis and Statistical Arbitrage: This course covers time series models, econometric aspects of financial markets, and statistical arbitrage trading strategies.
• Trends in Sell-Side Modeling: XVA, Capital, and Credit Derivatives: This course explores technical and regulatory aspects of counterparty credit risk, with an emphasis on model building and computational methods.
• Active Portfolio Management: This course covers the theoretical aspects of portfolio construction and optimization, with a focus on advanced techniques and econometric issues.
• Advanced Risk Management: This course gives a broad overview of the field of financial risk management, covering techniques for measuring and managing the risk of trading and investment positions.
• Advanced Topics in Equity Derivatives: This half-semester course covers advanced topics in equity derivatives, including volatility and correlation modeling, and exotic options and structured products.
• Algorithmic Trading and Quantitative Strategies: This course develops a quantitative investment and trading framework, covering mechanics of trading, high-frequency data, and simulation techniques.
• Interest Rate and FX Models: This course covers fixed-income models and foreign exchange derivatives markets, with an emphasis on practical aspects of modeling and valuation.
• Market Microstructure: This half-semester course covers topics of interest to both buy-side traders and sell-side execution quants, including market impact estimation and optimal execution strategies.
• Modeling and Risk Management of Bonds and Securitized Products: This half-semester course is designed for students interested in fixed income roles, covering modeling and risk management of bonds and securitized products.
• Cryptocurrency and Blockchains: Mathematics and Technologies: This half-semester course examines the building technologies and concepts in distributed ledger technologies and crypto financial markets.
• Trading Energy Derivatives: This course provides a comprehensive overview of quantitative strategies in energy markets, covering theories of storage, net hedging pressure, and derivatives pricing models.
• Scientific Computing in Finance: This course covers software and algorithmic tools necessary for practical numerical calculation in modern quantitative finance.

Faculty

The program is taught by academic and industry leaders, renowned mathematicians, and leaders at the world's most impactful financial institutions. The faculty brings real-time insight, real-life experience, and real-world connections into the classroom every day.

Career Services

The program has a strong track record of placement, with 95% of graduates placed in leading financial industry positions within three months of graduation. The career services team provides support and resources to help students achieve their career goals.

Alumni

The program's alumni are thriving at the cutting edge of the financial industry, with many holding senior leadership positions at top financial institutions. The alumni network provides a valuable resource for current students and recent graduates, offering mentorship, guidance, and job opportunities.