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.

To apply for the MCF Master degree program send us your CV in English, and a proof that you have previously completed the university-level academic studies for the minimal duration of 4 years (240 ECTS). A candidate that has completed a university education outside of Serbia needs to provide us, also, with a document certifying the equivalence of her degree to a degree in Serbia (this is called convalidation or nostrification) before entering into the Master degree program. Convalidation is done by an accredited university in Serbia (say, Union Unversity).

To apply for the MCF Short-cycle program just send us your CV in English. One does not need to have completed a 4-year university education or to nostrify diploma in order to enter into the Short-cycle program. After completing the Short-cycle program those participants that satisfy entry requirements into the Master degree program can complete the Master degree program by writing and defending a Master thesis (in English). No additional fees are assessed for completing the Master degree program after you complete the Short-cycle program.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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 beginning of the program, we organize an approximately two month preparation course where students can learn programming (Python, some R), math and probability theory that are needed for computational finance. By the end of the course students should be able to confidently write codes in Python and R addressing typical problems in finance and economics, working efficiently with financial data and creating sophisticated data visualizations. Students will understand some of the key concepts in calculus, linear algebra and probability and learn how they apply to challenges that we face in finance and economics. No prior programing knowledge is assumed. Bootcamp does not earn ECTS credits.

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.