Quantitative Finance and Risk Analytics

The Master of Science in Quantitative Finance and Risk Analytics at Rensselaer Polytechnic Institute is a rigorous, research-driven program designed to equip students with advanced knowledge and technical skills necessary to excel in the dynamic fields of finance and risk management. The program combines a strong foundation in financial theory with practical application of quantitative methods, data analysis, and computational techniques. Students will develop expertise in mathematical modeling, statistical analysis, and algorithmic trading strategies, enabling them to analyze complex financial products and assess various types of risks faced by modern financial institutions. Emphasizing a multidisciplinary approach, the curriculum covers stochastic calculus, financial engineering, derivative pricing, and credit risk modeling, alongside courses in data science, machine learning, and programming languages such as Python, R, and MATLAB.

Throughout their studies, students will engage in hands-on projects and real-world case studies that prepare them for careers in hedge funds, investment banks, asset management firms, insurance companies, and regulatory agencies. The program also offers opportunities for collaboration with industry partners, internships, and research initiatives led by renowned faculty members who are experts in quantitative finance and risk analytics. Rensselaer's state-of-the-art facilities and proximity to financial hubs provide an ideal environment for gaining practical experience and building professional networks. Graduates of this program will be well-positioned to pursue roles as quantitative analysts, risk managers, financial engineers, and data scientists, contributing innovative solutions to complex financial challenges in a fast-paced, data-driven industry. The program emphasizes ethical practices, decision-making under uncertainty, and the importance of regulatory compliance, preparing students not only to excel technically but also to serve responsibly in their professional careers. With a comprehensive curriculum aligned with industry standards and emerging trends, the Master of Science in Quantitative Finance and Risk Analytics at Rensselaer Polytechnic Institute offers a transformative educational experience for aspiring finance professionals.

Foundation Courses (Four Required)

Foundation courses are basic courses in finance and quantitative methods that contain concepts that are prerequisite to understanding the principles of Financial Engineering and Risk Analytics. These foundation courses are required for all students without undergraduate business degrees and for students whose backgrounds did not include coverage of comparable material. Students in Financial Engineering and Risk Analytics with business undergraduate degrees may waive the Financial Management I course with the consent of the Program Adviser(s) if they have sufficient relevant undergraduate work.  A student can waive this foundation course if he or she got a B or better in an introductory finance course  as well as a B or better in one higher level finance course. If this or any other course is waived, it must be replaced with some other course from the courses listed below. Therefore, waivers of any of these Foundation Courses will require substitutions from the Track Courses lists.

Foundation Course 1

• MGMT 6020 - Financial Management I Credit Hours: 3

• Or one of the following:

• MGMT 6190 - Introduction to Accounting and Financial Management Credit Hours: 3
• MGMT 6240 - Financial Trading and Investing Credit Hours: 3
• MGMT 6410 - Investments I Credit Hours: 3

Foundation Course 2

• MGMT 6520 - Financial Modeling Credit Hours: 3

• Or the following:

• MATH 4740 - Introduction to Financial Mathematics and Engineering Credit Hours: 4

Foundation Course 3

• MGMT 6510 - Financial Computation Credit Hours: 3

• Or one of the following:

• CSCI 4800 - Numerical Computing Credit Hours: 4
• MATH 4800 - Numerical Computing Credit Hours: 4
• CSCI 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4
• MATH 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4

Foundation Course 4

• MGMT 6370 - Options, Futures, and Derivatives Markets Credit Hours: 3

Concentration Courses (Six Required)

QFRA students are permitted to declare a Concentration in either the General Track, Quantitative Finance, or Financial Analysis. Students should select six (6) courses from the following lists to fulfill their concentration requirements.

General Track: Choose Six

• CSCI 4800 - Numerical Computing Credit Hours: 4
• CSCI 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4
• ECON 4570 - Econometrics Credit Hours: 4
• ECON 6570 - Advanced Econometrics Credit Hours: 3
• ECSE 6550 - Stochastic Processes in Communication and Control Credit Hours: 3
• ISYE 4760 - Mathematical Statistics Credit Hours: 4
• ISYE 6010 - Applied Regression Analysis Credit Hours: 3
• ISYE 6100 - Time Series Analysis Credit Hours: 3
• ISYE 6770 - Linear Programming Credit Hours: 4
• ISYE 6780 - Nonlinear Programming Credit Hours: 4
• ISYE 6870 - Introduction to Neural Networks Credit Hours: 3
• MATH 4800 - Numerical Computing Credit Hours: 4
• MATH 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4
• MATH 6660 - Stochastic Processes and Modeling Credit Hours: 4
• MATH 6740 - Financial Mathematics and Simulation Credit Hours: 4
• MATP 4600 - Probability Theory and Applications Credit Hours: 4
• MATP 4620 - Mathematical Statistics Credit Hours: 4
• MATP 6610 - Computational Optimization Credit Hours: 4
• MATP 6600 - Nonlinear Programming Credit Hours: 4
• MATP 6640 - Linear Programming Credit Hours: 4
• MGMT 4260 - Financial Statement Analysis Credit Hours: 3
• MGMT 4330 - Investments II Credit Hours: 4
• MGMT 4370 - Risk Management Credit Hours: 4
• MGMT 4440 - Financial Simulation Credit Hours: 4
• MGMT 6440 - Financial Simulation Credit Hours: 3
• MGMT 6030 - Financial Management II Credit Hours: 3
• MGMT 6240 - Financial Trading and Investing Credit Hours: 3
• MGMT 6340 - Financial Markets and Institutions Credit Hours: 3
• MGMT 6360 - International Finance Credit Hours: 3
• MGMT 6380 - Advanced Corporate Finance Credit Hours: 3
• MGMT 6400 - Financial Econometrics Modeling Credit Hours: 3
• MGMT 6410 - Investments I Credit Hours: 3
• MGMT 6430 - Financial Statement Analysis Credit Hours: 3
• MGMT 6940 - Independent Study Credit Hours: 1 to 6
• MGMT 7740 - Accounting for Reporting and Control Credit Hours: 3
• MGMT 7760 - Risk Management Credit Hours: 3

Quantitative Finance Track: Choose Six

• CSCI 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4
• ECON 4570 - Econometrics Credit Hours: 4

• (See * below)

• ECON 6570 - Advanced Econometrics Credit Hours: 3
• ECSE 6550 - Stochastic Processes in Communication and Control Credit Hours: 3
• ISYE 4760 - Mathematical Statistics Credit Hours: 4
• ISYE 6010 - Applied Regression Analysis Credit Hours: 3
• ISYE 6100 - Time Series Analysis Credit Hours: 3
• ISYE 6780 - Nonlinear Programming Credit Hours: 4
• MATH 4740 - Introduction to Financial Mathematics and Engineering Credit Hours: 4
• MATH 4800 - Numerical Computing Credit Hours: 4
• MATH 4820 - Introduction to Numerical Methods for Differential Equations Credit Hours: 4
• MATH 6660 - Stochastic Processes and Modeling Credit Hours: 4
• MATH 6740 - Financial Mathematics and Simulation Credit Hours: 4
• MATP 4600 - Probability Theory and Applications Credit Hours: 4
• MATP 4620 - Mathematical Statistics Credit Hours: 4
• MATP 6600 - Nonlinear Programming Credit Hours: 4
• MATP 6610 - Computational Optimization Credit Hours: 4
• MATP 6640 - Linear Programming Credit Hours: 4
• MGMT 4440 - Financial Simulation Credit Hours: 4
• MGMT 6440 - Financial Simulation Credit Hours: 3
• MGMT 6240 - Financial Trading and Investing Credit Hours: 3
• MGMT 6400 - Financial Econometrics Modeling Credit Hours: 3
• MGMT 6410 - Investments I Credit Hours: 3
• MGMT 6940 - Independent Study Credit Hours: 1 to 6
• MGMT 7760 - Risk Management Credit Hours: 3

Financial Risk Analysis Track: Choose Six

• ISYE 6180 - Knowledge Discovery with Data Mining Credit Hours: 3
• ISYE 6770 - Linear Programming Credit Hours: 4
• MATP 6640 - Linear Programming Credit Hours: 4
• MGMT 6030 - Financial Management II Credit Hours: 3
• MGMT 6240 - Financial Trading and Investing Credit Hours: 3
• MGMT 6340 - Financial Markets and Institutions Credit Hours: 3
• MGMT 6360 - International Finance Credit Hours: 3
• MGMT 6380 - Advanced Corporate Finance Credit Hours: 3
• MGMT 6400 - Financial Econometrics Modeling Credit Hours: 3
• MGMT 6410 - Investments I Credit Hours: 3
• MGMT 6430 - Financial Statement Analysis Credit Hours: 3
• MGMT 6940 - Independent Study Credit Hours: 1 to 6
• MGMT 7640 - Hedge Funds and Financial Markets Credit Hours: 3
• MGMT 7740 - Accounting for Reporting and Control Credit Hours: 3
• MGMT 7760 - Risk Management Credit Hours: 3

Footnotes

* The Graduate level offering of this course will normally require additional and/or more rigorous assignments from undergraduate students. The extra work will be specified in the syllabus of the course.

Requirements

1. Nonrefundable application processing fee of $75.
2. Statement of Background and Goals
3. Resume or curriculum vitae.
4. Two letters of recommendation.
5. Writing samples, if required by department.
6. IT Background Evaluation form (IT only).
7. Copies of official test scores (GRE, GMAT, TOEFL, IELTS, and PTE)
8. Copies of official transcripts and evidence of degrees earned, in English and in the native language, of all post-secondary education (including transcript keys)
9. Copy of your passport (international students only)
10. GMAT (preferred), GRE accepted
11. Statement of Background and Goals
12. TOEFL score of 230 CBT/88 iBT/570 PBT (IELTS 6.5 or PTE 60)
13. TOEFL speaking subscore of 22 or IELTS speaking subscore of 6.5

Scholarships

Teaching Assistants

Students assist Rensselaer faculty in their classroom and laboratory activities, gaining valuable experience as researchers, scholars, and teachers. Departments provide stipends and full-tuition waivers. Master’s students may spend a maximum of one year with internal support; doctoral students may spend a maximum of two years with internal support. Continued support can then be provided by means of research assistantships.

Research Assistants

Students work with the faculty in research-related tasks that further the student’s own graduate career and development as a researcher, scholar, and professional. Research assistants are paid a stipend and are given a full waiver of tuition.

Graduate Fellowships

Outstanding students may be awarded a university-supported Rensselaer Graduate Fellowship Award, which carries a full-tuition and fees scholarship and a minimum stipend of $21,500 per academic year. Students are nominated by their departments for Rensselaer Graduate Fellowship consideration.

Graduate Education Program (Russia)

Selected GEP Scholarship students are eligible for a renewable scholarship in the amount of 1,38 mln. rubles per year. Students in science and engineering fields are eligible for GEP funding. For application details and deadlines, please visit http://educationglobal.ru/en/. Applicants must be admitted to Rensselaer prior to finalizing the application for the GEP program.

The MSc in Quantitative Finance and Risk Analytics at Rensselaer Polytechnic Institute is a comprehensive program designed to prepare students for high-level careers in finance, risk management, and quantitative analysis. This program combines rigorous coursework in financial theory, models, and computation with practical applications in the financial industry. Students will gain expertise in areas such as financial derivatives, stochastic processes, financial econometrics, and risk measurement techniques. The curriculum emphasizes the development of strong quantitative skills, programming proficiency, and an understanding of market mechanics, enabling graduates to analyze complex financial products and make data-driven decisions. The program also incorporates modern data analytics, machine learning, and algorithmic trading strategies, reflecting the evolving landscape of finance. Students have access to state-of-the-art facilities, including financial laboratories equipped with advanced software and computing resources. Collaboration with industry partners, internships, and research projects are integral components, offering hands-on experience and networking opportunities. Rensselaer’s faculty members are experts in finance, mathematics, computer science, and economics, ensuring a multidisciplinary learning environment. The program is suitable for students with backgrounds in mathematics, engineering, computer science, or economics who seek to deepen their knowledge of quantitative finance. Graduates of this program are well-prepared for roles such as quantitative analysts, risk managers, financial engineers, and data scientists in investment banks, hedge funds, consulting firms, and financial technology companies. The program also provides a solid foundation for those interested in pursuing doctoral studies or research careers in related fields. Overall, the MSc in Quantitative Finance and Risk Analytics at Rensselaer Polytechnic Institute combines theoretical foundations with applied skills, making it a leading choice for aspiring finance professionals aiming to excel in an increasingly data-driven industry.