Quantitative Finance Program at State University of New York, Stony Brook

General Description

The Department of Applied Mathematics and Statistics (AMS) at Stony Brook University (SBU), New York (NY) offers masters (MS) and PhD degrees with a concentration in Quantitative Finance (QF). Details can be found in the QF website, see, 

https://www.stonybrook.edu/commcms/ams/graduate/qf/

The QF program is designed for students with a solid mathematical background.  The QF program involves various aspects of quantitative finance, statistics, optimizations, machine learning, and operations research. 

Why Should You Apply?

Top Ranking of the Program: AMS MS in Quantitative Finance ranked 4th in nation by Master-of-Finance.Org in 2020, see,
https://www.master-of-finance.org/best/great-master-of-financial-engineering-programs
The ranking is based on high salaries of graduates vs tuition costs. 

Jobs in Financial Industry: Most graduates of the QF program seek and obtain employment in the financial industry. Quantitative analysts in the US enjoy high salaries; see, e.g.,
https://www.indeed.com/salaries/Quantitative-Analyst-Salaries
It takes about 2-3 years of employment to pay back the education costs. 

Relatively Low Cost: For the SBU MS degree, a student needs 36 credits, in 2021 at a total tuition cost of $44,587 (out-of-state) and $26,902 (in-state), which is a savings of about 70% out-of-state tuition when comparing to other masters programs in the nearby New York City (90 minutes away by train). For more information on tuition costs, see, https://www.stonybrook.edu/commcms/sfs/
See also the Frequently Asked Questions (FAQ) web page of QF AMS,
https://www.stonybrook.edu/commcms/ams/graduate/qf/faq#view-application

Affordable Housing: Furthermore, housing is relatively inexpensive at SBU, compared to Manhattan in New York City.  The on-campus housing cost for any typical student was $9,082 in 2020 – 2021, and the price of a typical meal plan was $5,196; for reference, see,
https://www.collegefactual.com/colleges/stony-brook-university/paying-for-college/room-and-board/

Safe and Convenient Location & Friendly Environment: SBU is on Long Island, NY, about 50 miles east of the Manhattan financial center in NY City. The Long Island Rail Road links Stony Brook to Penn Station in Manhattan, and other locations. Stony Brook is the home of the prominent Renaissance Technologies hedge fund, see, https://en.wikipedia.org/wiki/Renaissance_Technologies 

Factors Helping QF Graduates to Find a Job

General Admission Requirements: The main requirement to be accepted to the QF program is a good knowledge of mathematics (calculus, probability, statistics, and optimization). Truly exceptional students are accepted to the PhD program with financial support.

Specific Admission Requirements: For admissions to the graduate study at AMS, the student must have a bachelor’s degree in mathematics, statistics, engineering, the physical sciences, or in the life or social sciences with a strong mathematics background. Additionally, foreign students must take the TOEFL exam (score ≥ 80 for MS and ≥ 90 for PhD), GRE exam is NOT required in 2022. 

Link to the AMS admission requirements:

https://www.stonybrook.edu/commcms/ams/graduate/resources/applying-to-the-AMS-grad-program.php

A foreign student should apply for an F-1 visa in order to study in the USA. Having an admission letter from the University, the application for F-1 visa is usually straightforward.

For Spring admission, international students may apply up to October 15.  For Fall admission, all students applying for PhD with support must submit their application by December 15; international students that are not applying for support may apply up to April 1. 

Courses Offered in QF Program: The courses offered by the QF program covers broad range of topics on both theoretical and practical features of quantitative finance. The following is a list of required courses for the MS degree in the QF program.  

  • AMS 507 Introduction to Probability
  • AMS 510 Analytical Methods for Applied Mathematics and Statistics
  • AMS 511 Foundations of Quantitative Finance
  • AMS 512 Portfolio Theory
  • AMS 513 Financial Derivatives and Stochastic Calculus
  • AMS 514 Computational Finance
  • AMS 516 Statistical Methods in Finance
  • AMS 517 Quantitative Risk Management
  • AMS 518 Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization   
  • AMS 572 Data Analysis (can be replaced by AMS 520 Machine Learning in Quantitative Finance, for those who have already taken an equivalent data analysis course before)

Besides required courses, M.S. students in the QF program also need to take at least two elective courses from the following list.

  • AMS 515 Case Studies in Computational Finance 
  • AMS 520 Machine Learning in Quantitative Finance
  • AMS 522 Bayesian Methods in Finance
  • AMS 523 Mathematics of High Frequency Finance
  • AMS 526 Numerical Analysis I 
  • AMS 527 Numerical Analysis II 
  • AMS 528 Numerical Analysis III 
  • AMS 530 Principles of Parallel Computing 
  • AMS 540 Linear Programming
  • AMS 542 Analysis of Algorithms
  • AMS 550 Stochastic Models
  • AMS 553 Simulation and Modeling
  • AMS 560 Big Data Systems, Algorithms and Networks
  • AMS 561 Introduction to Computational and Data Science
  • AMS 562 Introduction to Scientific Programming in C++
  • AMS 569 Probability Theory I
  • AMS 570 Introduction to Mathematical Statistics
  • AMS 578 Regression Theory
  • AMS 580 Statistical Learning
  • AMS 588 Failure and Survival Data Analysis
  • AMS 595 Fundamentals of Computing
  • AMS 603 Risk Measures for Finance and Data Analysis

The following is an example of quantitative finance course offered by the QF program.

AMS 517 Quantitative risk management. The course will present various techniques and methods for the analysis of market risk, credit risk, and operational risk in the financial market. In particular, methods for market risk covered in the course include value at risk, coherent risk measures, time series models, dimension reduction techniques, extreme value theory, and Monte Carlo methods; methods for credit risk and operational risk include survival analysis, generalized linear models, structural and reduced form models, and Poisson process models.