APPLY FOR MCF

MASTER IN COMPUTATIONAL FINANCE

HOW CAN YOU GET IT?

There are two versions of the Master in Computational Finance (MCF) programs: Short-Cycle and Master degree program. The coursework and cost of the two versions of the program is identical but there are important differences as well. Read on.

Master in Computational Finance (MCF)

We are looking for talented and creative individuals interested in developing world-class expertise in the field of computational finance and becoming active players in the regional and global marketplace. While strong quantitative aptitude is beneficial, there are no formal requirements in terms of the background or a major. What is crucial is curiosity and motivation.

We are seeking ambitious individuals eager to develop cutting-edge expertise in computational finance. Our program is designed for those who aspire to become leaders in the evolving global and regional financial landscapes, equipped with skills that extend beyond traditional finance into programming, data science, and AI applications.
While a strong quantitative aptitude is an advantage, we welcome candidates from diverse academic and professional backgrounds from around the world. No specific major or prior experience in finance is required. What matters most is a passion for learning, curiosity to explore new technologies, and the motivation to tackle complex real-world challenges.
If you’re ready to embrace the integration of finance with Python and R programming, machine learning, and generative AI systems—and unlock their potential in solving real-world business challenges—this program is for you.

 

 

Financial Computing and Quantitative Investments

The aim of the course is to enable participants to understand and implement in Python key concepts in investment science and quantitative investment strategies.

By the end of the course, participants should acquire the tools required for making sound investment decisions. They should be able to understand both the foundational theory and underlying concepts, as well as learn how to practically apply these concepts in Python in all stages of the work flow.

This course is required for all participants. It carries 6 ECTS credits.
Financial Derivatives

The goal of the course is to develop firm understanding of the principal ideas and models that underpin modern financial practice and theory and to build hands-on experience in valuation, hedging and trading of financial derivatives using Python.

At the end of the course, students will understand the institutional aspects and methods of valuation and hedging of derivative securities in discrete and continuous time, effectively utilize data on financial derivatives and implement derivative valuation and hedging methods in Python. They shall also test their derivative-based investment strategies using a realistic trading simulator.

This course is required for all participants. It carries 6 ECTS credits.
Statistics and Financial Data Analysis

The course provides a comprehensive introduction to key concepts used in applied statistical work with financial data. The emphasis is both on the key principles of the u nderlying statistical theory as well as on the economic intuition behind the estimates.

At the end of the course, the students will have a good understanding of the “traditional” statistical methods for financial data analysis (outside the machine learning framework), their merits and disadvantages, and will be well equipped to conduct individual data-based research or industry projects. In addition, they will be able to implement these concepts in R.

This course is required for all participants. It carries 6 ECTS credits.
Machine Learning

The course provides a comprehensive introduction to the most important machine learning models and algorithms and their applications in finance, with an emphasis on model performance, validation, and interpretability and their implementation in Python.

At the end of the course, the students will have a good understanding of the most important (supervised and unsupervised) machine learning algorithms and their applications. They will know which models are suitable for a given problem and data set, how to evaluate the model quality, and how to interpret the results. In addition, they will have implemented a number of models using large sets of financial data.

This course is required for all participants. It carries 6 ECTS credits.
Fixed Income and Credit

The objective of the course is to develop understanding of fixed income securities and markets as well as interest rate derivatives. We study valuation and hedging using these instruments and discuss how these methods are used in practice.

At the end of the course, students will understand the institutional aspects and methods of valuation of fixed income securities such as bonds and related instruments, construction of yield curves, valuation and hedging using interest rate derivatives. In addition, they will be able to implement these models in Python.

This course is required for all participants. It carries 3 ECTS credits.
Topics in Financial Technologies

The aim of the course is to study interconnection between new technology and finance and provide fundamental understanding of digital innovation of financial intermediation functions (money, payments, capital raising, market aggregation, price discovery) with emphasis on scalable business models and consumer products.

Upon the completion of the course participants should be familiar with principal varieties of Fintech ecosystem and what and how is disrupted in traditional money, payment, lending, banking and investment industries. Participants should be able to understand the basic requirements and features of technology, data and algorithms underpinning the Fintech industry. They should be able to understand specific management challenges in such ventures including regulation, management of customers, value propositions, and best practices. Finally, they shall get a chance to structure thinking about starting a new business in this industry.

This course is elective. It carries 6 ECTS credits.
Algorithmic Trading, Blockchain, and Decentralized Finance (DeFi)

This course provides an introduction to the blockchain technology, cryptocurrencies, retail trading and algorithmic trading. A Technical Analysis and Trading Systems will be introduced as a necessary basis for retail and algorithmic trading on cryptocurrency and other markets. In addition, participants receive hands-on introduction to creating blockchain applications and what kinds of Decentralized Finance ecosystems, technologies, tools, products and techniques exist, with strong focus on Ethereum as a case study.

At the end of the course, participants will learn how to apply Technical Analysis on cryptocurrencies, but also on many other asset classes. They will be able to create and use Technical Indicators, to assess the risk involved in trading, to apply sound money management techniques and to create profitable trading systems that can be applied for manual and algorithmic trading on various markets. Participants will learn the basics of Solidity and how to create tokens in Ethereum using that programming language. They will learn how to create ERC20 and ERC721 tokens on top of Ethereum. In other words they will know how to create fungible and non-fungible tokens.

This course is elective and carries 6 ECTS credits.
Investments

The course provides in-depth contextual understanding of the financial investing in conventional (with the focus on equity) and in alternative asset classes (private equity, venture capital, hedge funds, etc.) from the point of view of institutional investors. The course demonstrates best practice governance in a comprehensive investment transaction life cycle and with a particular emphasis on structuring and valuation segments.

After the completion of the course participants should be able to understand how to value financial (equity) investments and implement best practice governance in a full transaction life cycle. The covered concepts spotlight relevant induction on and first-hand experience in industry changing trends through growth and cyclical investing approaches, ESG investing, impact investing, mergers and acquisition, innovative transformations and venture funding, etc. This course offers a completing puzzle piece to the ground math and statistical modeling problems solving coursework.

This course is elective and carries 6 ECTS credits.
Quantitative Risk Management

The aim of the course is to provide practical, hands-on training for those interested in working in risk management and complete their preparation for passing the FRM and PRM certificates, two globally recognized certificates for risk managers.

By the end of the class students should be able to perform independent risk modeling and verifications of risk models across major risk classes, understand contemporary risk regulation and the role that regulatory and economic capital, cost of funding and fund transfer pricing play in modern financial institutions. In addition, they should know how to implement many of these models in R and/or Python.

This course is elective. It carries 6 ECTS credits.
Stochastic Calculus

The aim of this course is the provide solid working knowledge of stochastic differential and stochastic calculus since stochastic differential equations are used to model the behaviour of financial assets and stochastic calculus is the fundamental tool for understanding and manipulating these models.

At the end of the course, participants should be able to solve different types of linear stochastic differential equations, understand and apply Ito’s Lemma for scalar and vector stochastic processes, understand the connection between stochastic differential equations and partial differential equation of the heat equation type and perform many of needed calculations using both pen and paper as well as using symbolic and numerical computations.

This course is elective. It carries 6 ECTS credits.
Numerical Methods

The aim of the course is to provide a comprehensive and mathematically rigorous introduction to Monte Carlo and finite difference methods for pricing financial options and for evaluating their sensitivities to various input parameters.

At the end of the course, the student should have a thorough understanding of the basic theory behind Monte Carlo and finite difference methods, and be able to implement them in standard applications.

This course is elective. It carries 6 ECTS credits.

Before the program commences we offer a comprehensive two-month preparation course designed to build foundational skills in finance, programming, mathematics, probability, and applications of AI technologies – all essential for success in computational finance.

 

During the Bootcamp, students:

 

  • Develop confidence in coding with Python and R while solving real-world problems in finance and economics.
  • Understand essential financial concepts, learn to efficiently process and analyze financial data and create advanced data visualizations.
  • Gain a solid grasp of essential concepts in calculus, linear algebra, and probability, with a focus on their practical applications in finance and economics.
  • Explore generative AI systems and learn to apply these technologies in problem-solving, technical writing, code debugging, and creating innovative solutions to financial and economic challenges.

 

No prior finance or programming knowledge is required, making this course an excellent starting point for individuals from diverse academic backgrounds. Please note that the Bootcamp does not carry ECTS credits but serves as an invaluable foundation for the main program.

Upon the completion of the MCF Master degree program one receives the academic title Master in Computer Science-Computational Finance from the School of Computing, Union University, Belgrade. The program is accredited as the official Master program in the Republic of Serbia.

Upon completion of the MCF Short-cycle program one receives the Certificate of Completion of the Master in Computational Finance (MCF) Short-Cycle program from the School of Computing, Union University, Belgrade. The program is approved by the National Council for Higher Education of the Republic of Serbia.

MCF Master degree program
KEY FACTS

Duration

12 months

Language of Instruction

English

Required Courses

5

Number of Electives to Complete

2 out of 6

Degree

Master in Computer Science – Computational Finance

Number of ECTS upon completion

60

Tuition fees

6000 EUR

Scholarships available

No

Financial aid available (tuition reduction)

Yes

APPLY

MCF Short-Cycle Program
KEY FACTS

Duration

12 months

Language of Instruction

English

Required Courses

5

Number of Electives to Complete

2 out of 6

Certificate

Master of Computational Finance (Short-Cycle) Certificate of Completion

Number of ECTS upon completion

39

Tuition fees

6000 EUR

Scholarships available

Yes

Financial aid available (tuition reduction)

Yes

APPLY

3 STEPS TO ENTER THE PROGRAM

Apply online

Interview with
the Program Director

Acceptance Letter
via email

The MCF Program and the School of Computing are proud to announce a new Academic Partnership with ARPM (New York City) — a globally recognized leader in quantitative finance education.

Founded by Attilio Meucci, Advanced Risk and Portfolio Management (ARPM) is recognized for its innovative approach to risk and portfolio management, offering advanced tools and resources for finance professionals. This collaboration provides MCF students exclusive access to the ARPM Lab, which houses a wealth of materials, including theory, case studies, code, animations, slides, and proofs, all designed to enhance students’ understanding and practical skills in quantitative finance.

Through this strategic partnership, MCF students will gain insights into advanced quantitative methods, ensuring they are equipped with the tools necessary for success in the fast-paced world of finance, as well as direct networking opportunities on Wall Street.

 

Find out more: www.arpm.co

The MCF program and the School of Computing have partnered with the CQF Institute, offering complimentary memberships and access to exclusive resources, events, and networking opportunities in quantitative finance.

Master of Computational Finance and the School of Computing are proud to announce our partnership with the CQF Institute. The CQF Institute is the fastest-growing quant and computational finance membership organization and the awarding body for the Certificate in Quantitative Finance (CQF).

This collaboration heralds a new era of academic enrichment and professional development of the MCF Program and the School of Computing, who will now enjoy complimentary membership to the Institute. This membership opens the door to a wealth of resources including CPD-accredited events, exclusive conferences, and technical talks, as well as unparalleled opportunities for networking and career advancement.

Through this partnership, the CQF Institute reaffirms its commitment to nurturing the next generation of finance talent by providing students with the tools and insights necessary to thrive in the fast-paced world of quantitative finance, enhancing their educational journey with practical, industry-leading expertise.

Download PDF brochure (3.1 Mb)

While completing the MCF program, you can obtain, also, a Master Certificate in Computational Finance from Collegio Carlo Alberto, Torino, Italy.

The Collegio Carlo Alberto (CCP), located in Torino, Italy, is a foundation created in 2004 as a joint initiative of the Compagnia di San Paolo and the University of Torino with the purpose of establishing cutting-edge education and research programs in finance, economics and broader social sciences. Today, CCP is one of the leading centers of excellence in economic and finance research and education in Europe. Our cooperation with CCP is the first of its kind for the Collegio and reflects high international demand for the level and mix of skills that the MCF program provides. Importantly, the agreement creates a direct link between the MCF program participants and the leading Italian and European financial institutions.


According to the terms of the agreement, upon the completion of the MCF program each program participant can, without additional cost, earn the Collegio’s Master in Computational Finance certificate as well. For this, she needs to attend two additional short courses (typically one or two days long) taught by distinguished finance professionals selected by the Collegio. In addition, they can participate in the Collegio’s Career Days organized by their sponsors – leading Italian and European financial and fintech firms. This allows them to meet the same prospective employers as the CCA students. Both the courses and the career day participation is currently online. Master certificates of CCP are broadly recognized among businesses in Italy and worldwide. Please contact us if you need more information.

FREQUENTLY ASKED QUESTIONS

Yes, you can.
From December to February.
No, but it is highly recommended. It allows us to create a common set of experiences and knowledge across the cohort of students.
The first semester is from February to July and the second semester is from September to December.
Both the master degree and short-cycle program applicants that are highly motivated to participate in the program but cannot afford to pay the full tuition and fees are encouraged to apply for the financial aid in form a tuition reduction. The reduction can be up to 50 percent of the full tuition and fees. This issue should be discussed with the Program Director.
Participants of the Short-Cycle program only. If a scholarship is granted, it covers the entire tuition and fees for the program.

Ultimately, that decision is made by the company providing the grant.

Yes, provided that you satisfy the formal conditions to enter the MCF Master Degree program. In that case, after you complete the Short-Cycle program you need to complete and defend, in addition, a Master thesis in English. No additional fees are assessed with respect to the amount paid for the Short-Cycle program.
Of course. In 2021/2022 we have had participants from Austria, Czech Republic, Holland, Nigeria, and Switzerland. The same terms and conditions apply to Serbian and foreign applicants. For an applicant with a foreign academic degree interested in the Master degree program, it makes sense to apply to the Short-Cycle program and at the same time work on the recognizion on your degree in Serbia. Once the nostrification of your degree is obtained and you complete the Short-Cycle program, you can enter cost-free into the Master degree program, and write and defend the Master thesis.
Yes. You can follow the entire program remotely. While some of the classes are taught in the lecture hall at the School of Computing, they are also available online in real time. Classes taught by teachers living abroad are mostly taught just online. Each lecture is taped and is available for later perusal of the program participants.
Yes. The classes are organized from 6 to 9 pm CET (approximately, with breaks), up to 3 times per week. One need to, also, plan some time to work on practical group projects. If you have to miss a class, you can watch it later since all of them are taped for your convenience.
Yes, on all three accounts. Our program covers a substantial fraction of the CFA Body of Knowledge (CBOK) for investment analysists. In addition, it covers the large fraction of the material needed for passing either GARP or PRMIA exams for risk managers. Given our combination of rigorous academic training and development of practical skills, we argue that our program is, indeed, one of the best ways to prepare for either of these three global professional certifications.

LET’S TALK

All your questions about MCF will be answered promptly.
You just have to ask.
CONTACT US

Copyright ©2022 RAF

FOLLOW US

Copyright ©2022 RAF

Powered by: GSM solutions

APPLY FOR MCF

FOLLOW US