Stony Brook University - Master/PhD in Quantitative Finance

Masters Degree
Published

February 9, 2024

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

Key takeaways

  • 2 year program
  • tuition fee $25,000
  • quantitatively focused course works
  • non-target school

Program

Spring

Deadline: October, December

Fall

Deadline: May, June

Fees

Direct Costs New York Resident Out of State Resident
Tuition $11,310 $24,490
Fees $2,632 $2,632
Housing $10,630 $10,630
Meals $7,050 $7,050
Total Direct Cost $31,622 $44,802

Prerequisite/requirements

Admissions criteria

minimum cumulative grade point average of 3.00 on a 4.00 point scale

Personal statement

Provide a brief personal statement regarding your experience and interest in the program.

Application fee

$100

Letter of recommendation

3 letters required

  • Paper recommendations can be sent in, but you must use the paper recommendation form from your application portal and give it to your recommender. Paper recommendations must be sent directly to the graduate program you are applying to in sealed envelopes, signed across the seal by the recommender.
  • In order to gain access to our recommendation forms, you must submit an application. Within your application, you can either send emails directing your recommenders to each submit a recommendation online, or you may download a paper recommendation form and give it to your recommender. Paper recommendations must be sent directly to the graduate program you are applying to in sealed envelopes, signed across the seal by the recommender.

Transcript

They have an official seal/stamp and an official signature by an appropriate academic administrative officer. They are sealed by the university.

GRE scores

required

English proficiency scores

TOEFL iBT Speak IELTS Speak Course Requirement Result
23-30 7 or higher none Eligible to TA
21-22 6.5 OAE 594 Eligible to TA
18-20 6 OAE 592 Eligible to run recitation and lab sessions and/or grade
15-17 5-5.5 OAE 590 Eligible to TA

Curriculum

Standard 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

Quantitative finance track electives

  • 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

(A) Typical course sequence:  Modeling 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

(C) Typical course sequence:  Statistics and data analytics

**(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)

Photo by Hudson Graves on Unsplash