Quantitative Finance MS and PhD

The Stony Brook Department of Applied Mathematics and Statistics offers MS and PhD STEM designated training in quantitative finance.Summary of QF program for potential students is available at QF chair webpage. Because of the strong demand, admission is highly competitive at both the MS and PhD levels in quantitative finance. The department prepares practitioners who apply mathematical and computational methods to develop and exploit financial opportunities for return enhancement and risk control. The department, one of the country's leading applied mathematics departments, offers a range of related coursework in applied statistics, operations research, and computational science.

Implied Volatility Surface

Figure: Implied Volatility Surface

SPECIAL QUALITIES OF STONY BROOK QUANTITATIVE FINANCE PROGRAM

Most of the Applied Mathematics faculty teaching quantitative finance courses have extensive experience building quantitative trading systems on Wall Street. Because of their Wall Street backgrounds, our faculty are able to place many of their QF students in internships during the summer and the academic year at hedge funds and major investment companies. Few other QF programs offer internships. There is limited use of adjunct faculty who come to campus one or two evenings a week after work.

Merger Arbitrage New Image

Figure: Merger Arbitrage Strategy

In the world of finance, the name 'Stony Brook' is famous for Renaissance Technologies, which is located a mile from the Stony Brook campus and headed by the former chairman of the Stony Brook Mathematics Department. Renaissance's flagship Medallion Fund has been the best performing hedge fund in the world for the past 20 years. One of the key creative minds at Renaissance, Robert Frey, Stony Brook Applied Mathematics PhD 1986, returned to Stony Brook in 2005 after early retirement at Renaissance to develop a Quantitative Finance program in the Stony Brook Applied Mathematics Department. Frey is chairman of the advisory committee to the University of Chicago's mathematical finance program, the country's best-ranked program in this area.

The Stony Brook Quantitative Finance program is unique among mathematical sciences departments in its very practical focus on 'alpha generation', Wall Street term for trading strategies for making money. Courses are centered on projects where students use real tick data to analyze and predict the performance of individual stocks and commodities, market indices and derivatives. Also, Stony Brook is one of a small number of quantitative finance programs offering PhD as well as MS training. Our PhDs have taken positions both in Wall Street firms and in university quantitative finance programs. For more information about our quantitative finance courses and faculty, see QF Courses and QF People.

Wall Street

Figure: New York Stock Exchange

Course Requirements for the Quantitative Finance Track

The standard program of study for the M.S. degree specializing in quantitative finance consists of:

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

Quantitative Finance Track Electives (students must take at least 2 elective courses to achieve at least 36 graduate credits along with the required courses):

AMS 515 Case Studies in Machine Learning and 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 586 Time Series
AMS 595 Fundamentals of Computing
AMS 603 Risk Measures for Finance and Data Analysis

Elective courses in the QF program are split in five focus areas. Students can follow one of the following course sequences depending upon their interests.

(A) Typical course sequence: Modelling and risk management in finance

First Semester - AMS 507, 510, 511, 572 ( or Electives: AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
Second Semester - AMS 512, 513, 517 (Electives: AMS 515, 522, 523, 603)
Third Semester - AMS 514, 516, 518 (Electives: AMS 553)

(B) Typical course sequence: Machine learning and big data

First Semester - AMS 507, 510, 511, 572(or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
Second Semester - AMS 512, 513, 517 (Electives: AMS 515, 560, 580)
Third Semester - AMS 514, 516, 518 (Electives: AMS 586)

First Semester - AMS 507, 510, 511, 572(or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
Second Semester - AMS 512, 513, 517 (Electives: AMS 515, 570, 578 (with pre-requisite 572 )
Third Semester - AMS 514, 516, 518 (Electives: AMS 553, 586)

(D) Typical course sequence: Stochastic calculus, optimization, and operation research

First Semester - AMS 507, 510, 511, 572(or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
Second Semester - AMS 512, 513, 517 (Electives: AMS 515, 542, 550, 569)
Third Semester - AMS 514, 516, 518 (Electives: AMS 540, 553)

(E) Typical course sequence: Computational methods and algorithms

First Semester - AMS 507, 510, 511, 572(or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
Second Semester - AMS 512, 513, 517 (Electives: AMS 515, 527, 528, 561)
Third Semester - AMS 514, 516, 518 (Electives: AMS 530, 562, 526 (co-requisite or pre-requisite 595 or 561)

Note 1: If you have poor programming skills take the following electives (instead of electives recommended in sequences): AMS 595 Fundamentals of computing (Fall semester) or AMS 561 Introduction to computational and data science (Spring semester). Programming skills are critically important for industrial jobs.

Note 2: If a 4th semester becomes necessary, a required course will be needed to continue.

For Ph.D. requirements please click here.

Quantitative Finance Opportunities for Applied Mathematics Graduate Students in Other Tracks
Any strong student (3.5+ GPA in first-semester core courses) in another track may enroll in AMS 511, Foundations in Quantitative Finance. Selected students, with the permission of the Director of the Center for Quantitative Finance, may take additional quantitative finance courses. Students are eligible to earn an Advanced Certificate in Quantitative Finance. You must formally apply for the secondary certificate program prior to taking the required courses. Only a maximum of six credits taken prior to enrolling in the certificate program may be used towards the requirements. The 15-credit advanced certificate requires AMS 511, AMS 512, AMS 513, one additional Quantitative Finance Graduate course elective, and one additional Applied Mathematics course chosen with an advisor’s approval.

To apply, download the registration form, please click here.

Quantitative Finance