University of Michigan - Master of Science in Quantitative Finance and Risk Management

https://sites.lsa.umich.edu/quant/

QUANT – The Quantitative Finance and Risk Management Program

Key takeaways

• 2-year program provided
• tuition fee $28,000, $112,000 in total (4 semesters)
• dual degree opportunity
• many mathematical/statistical courses

 

Program

Deadline: February

 

Fees

$27,360 per semester

 

Prerequisites/requirements

Recommended undergraduate coursework

• Advanced Calculus
• Linear Algebra
• Probability
• Ordinary Differential Equations
• Partial Differential Equations
• Analysis
• Programming (Matlab and/or C/C++)

 

also valued:

• Financial Mathematics
• Finance
• Python

 

Application materials

• Statement of Purpose: The Statement of Purpose should be a concise, well-written essay about your mathematical background, your career goals, and how the Quantitative Finance and Risk Management program will help you meet your career and educational objectives.
• Personal Statement: Tell us about where you grew up, when you first decided you wanted to study quantitative finance, what challenges you faced and how you overcame them in your pursuit of your education and career.
• Resume: Please limit your resume to 1 page.
• Three letters of recommendation: only submit 2 letters of recommendation, though we recommend that both come from U-M faculty in mathematics or statistics.
• transcripts

 

GRE

not required, but encouraged

 

TOEFL or IELTS

For Quant Program applicants, your overall TOEFL core must be 95 or higher. Your overall IELTS score must be 7 or higher.

 

Curriculum

Semester 1

• Numerical Methods with Financial Applications
• Discrete State Stochastic Processes
• Advanced Financial Mathematics I
• Applied Statistics I / Statistical Learning I: Regression

 

Semester 2

• Advanced Financial Mathematics II
• Stochastic Analysis of Finance
• Statistical Analysis of Financial Data

 

Semester 3

• Computational Finance

 

Electives

Quant students choose 12 or more credits of electives (3 – 5 courses) from across the university

1. Complete online summer assignments prior to the start of the first term.
2. Attend orientation prior to the start of the first term.
3. Attend all mandatory seminars as directed by program administration.
4. International students: Successfully complete an English Language Institute course in the first and second terms of enrollment, unless waiver is granted by program administration.
5. Meet all of Rackham Graduate School’s academic requirements, including maintaining a cumulative GPA of 3.0 or higher.
6. Apply for graduation.

 

Math courses

• MATH 561/IOE 510: Linear Programming (3 cr, F/W)
• MATH 562/IOE 511: Continuous Optimization Methods (3 cr, W)
• MATH 597: Analysis II (3 cr, W)
• MATH 602: Real Analysis II (3 cr, F)
• Math 628/629: Machine Learning for Finance I/II (2 + 2 cr, W/F)
• MATH 663/IOE 611: Nonlinear Programming (3 cr)

 

Finance courses

• FIN 466: Real Estate investment (3 cr, W)
• FIN 551: Financial Management and Policy (3 cr, F/Su)
• FIN 575: Financial Modeling (1.5 cr)
• FIN 580: Financial Derivatives in Corporate Finance (2.25 cr, F/W)
• FIN 608: Capital Markets & Investment Strategies (2.25 cr, F)
• FIN 609: Fixed Income Securities and Markets (2.25 cr, F/W)
• FIN 612: International Finance Management I (1.5 cr, F)
• FIN 614: International Finance Management II (1.5 cr, F)
• FIN 631: Risk Management in Banks and Financial Institutions (2.25 cr)
• FIN 640: Financial Trading (1.5 cr)
• FIN 645: Real Options in Valuation (2.25 cr)
• FIN 725: Maize and Blue Fund (1.5 cr, F)
• FIN 726: Maize and Blue Fund (1.5 cr, W)

 

Economics courses

• ECON 411: Monetary and Financial Theory (3 cr)
• ECON 441: International Trade Theory (3 cr)
• ECON 442: International Finance (3 cr)
• ECON 501: Microeconomics (3 cr)
• ECON 502: Macroeconomics (3 cr)

 

Statistics courses

• STATS 415: Data Mining (4 cr, W)
• STATS 503: Statistical Learning II: Multivariate Analysis (3 cr, W)
• STATS 504: Statistical Consulting (3 cr, F)
• STATS 507: Data Science and Analytics Using Python (3 cr, W)
• STATS 531: Analysis of Time Series (3 cr, W)
• STATS 535: Reliability (3 cr)
• STATS 600: Linear Models (3 cr, F)
• STATS 607: Programming and Numerial Methods in Statistics (1.5 cr, F/W)

 

Computer science courses

• EECS 402: Programming for Scientists and Engineers (4 cr, F/W)
• EECS 477: Intro to Algorithms (4 cr)
• EECS 484: Database Management (4 cr, F/W)
• EECS 498: Special Topics (select sections)
• EECS 492: Introduction to Artificial Intelligence (4 cr)
• EECS 545: Machine Learning (3 cr, F/W)
• EECS 547: Electronic Commerce (3 cr)
• EECS 586: Design and Analysis of Algorithms (4 cr)
• EECS 592: Foundations of Artificial Intelligence (4 cr)
• EECS 595: Natural Language Processing (3 cr, W)
• EECS 597: Language and Information (3 cr)

 

Other elective courses

• BIOSTAT 615: Statistical Computing (3 cr, F)
• BIOSTAT 650: Applied Statics I: Linear Regression (4 cr, F)
• ENGR 599: Special Topics in Engineering – Multidisciplinary Design Projects (1-4 cr, F/W)
• HS 650: Predictive Analytics (4 cr, F)
• SI 507 : Intermediate Programming (3 cr, F/W)
• SI 618: Data Manipulation and Analysis (3 cr)
• TO 513: Spreadsheet Modeling and Applications (1.5 cr, F/W)
• TO 618: Applied Business Analytics and Decisions (3 cr, W)

 

Dual degree opportunities

• graduate certificate in data science
• Master of science in data science
• Master of science in applied economics
• Master of science in applied statistics

Photo by Mathew Schwartz on Unsplash