Curriculum
Master in Computational Finance (MCF)
Preparatory Bootcamp
A two-month preparatory course covering the fundamentals of finance, programming (Python and R), mathematics, and artificial intelligence - with no prior knowledge required.
Semester I
Required modules
Financial Computing and Quantitative Investments
Develops theoretical foundations and practical Python skills for investment science and quantitative investment strategies. Students combine financial theory with implementation and learn to build and evaluate data-driven investment strategies.
Financial Derivatives
Develops an understanding of the models and techniques used to value, hedge and trade financial derivatives. Students work with discrete and continuous-time models and implement valuation and hedging methods in Python.
Statistics and Financial Data Analysis
Introduces the main statistical methods used in financial data analysis. The course combines statistical theory with economic interpretation and practical implementation in R.
Fixed Income and Credit
Covers fixed-income securities, interest-rate markets, yield curves and interest-rate derivatives. Students learn valuation and hedging methods and implement relevant models in Python.
Machine Learning
Introduces the most important supervised and unsupervised machine-learning methods and their applications in finance. The course focuses on model selection, validation, performance, interpretability and Python implementation.
Semester II
Elective modules - choose 2
Topics in Financial Technologies
Explores the relationship between technology and financial services, including payments, lending, banking, investment and other areas being transformed by fintech.
Algorithmic Trading, Blockchain and Decentralized Finance
Introduces blockchain, cryptocurrencies, technical analysis and algorithmic trading, while exploring smart contracts and the technological foundations of decentralised financial systems.
Investments
Covers conventional and alternative asset classes including equities, private equity, venture capital and hedge funds, with an emphasis on the perspective of institutional investors.
Quantitative Risk Management
Provides practical training in financial risk modelling and risk-model validation, including topics relevant to professional risk-management qualifications.
Stochastic Calculus
Introduces stochastic differential equations, stochastic processes, Ito's Lemma and mathematical methods used to model financial asset behaviour.
Numerical Methods
Provides an introduction to Monte Carlo simulation and finite-difference methods used for pricing financial instruments and calculating sensitivities.
Admissions profile
Application and Entry Requirements
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.
Professional certifications
Unique preparation for CFA®, FRM® and PRMIA® (PRM™) exams
The MCF program provides uniquely high-quality education and comprehensive preparation for internationally recognized certifications - CFA®, FRM®, and PRMIA® (PRM™). None of our participants have ever failed these exams.
12
Months
60
ECTS total
5 + 2
Required + elective modules
16
ECTS thesis
Archival student video
Meet the MCF Students, Class of '21
An archival look at the program's first cohort and learning experience.
Watch on YouTube