https://www.stonybrook.edu/commcms/ams/graduate/qf/
Quantitative Finance | Applied Mathematics & Statistics
Applied Math and Statistics at Stony Brook University
www.stonybrook.edu
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
- 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)
