Specialisation
Quantitative Finance
A quantitative finance curriculum at master’s level: six core modules, two electives, three examinations and a capstone project. The curriculum is part of recognised academic degrees, from the Postgraduate Certificate to the PhD.
Professional quant qualifications define what a working quant must know. The SIDF Quantitative Finance specialisation has a comparable, practical structure, from stochastic calculus and derivatives pricing to machine learning. SIDF delivers this structure inside academic awards with credits: a Postgraduate Certificate (30 ECTS), a Master’s degree (90 ECTS) or a PhD.
We teach each topic for implementation. You work in Python from the first week, with real market data. At the end you have a portfolio of models that you built, tested and documented.
Three phases
Preparation
Optional online primers refresh the foundations before the core modules start: Mathematics for Finance, Python for Finance and Financial Markets Foundations. Faculty gives one-to-one support.
Qualification
Six core modules and two advanced electives. Three examinations and a final capstone project on a real problem assess your knowledge.
Lifelong learning
Alumni keep access to an updated library of lectures, code and seminars. They also stay in the SIDF community of quants, traders and regulators.
Six core modules
01Building Blocks of Quantitative Finance
- Probability, random walks and Brownian motion
- Applied Itô calculus and stochastic differential equations
- Martingales, Fokker-Planck and Kolmogorov equations
- Simulation and numerical methods in Python
02Quantitative Risk and Return
- Modern portfolio theory, CAPM and factor models
- Econometrics: ARMA, ARCH/GARCH and volatility estimation
- Value-at-Risk, expected shortfall and stress tests
- Risk regulation: Basel, FRTB and model governance
03Equities, Currencies and Derivatives
- Black-Scholes theory, delta hedging and no-arbitrage
- Volatility surfaces, smiles and local/stochastic volatility
- Numerical pricing: Monte Carlo, finite differences, trees
- Exotic and structured products, FX derivatives
04Data Science and Machine Learning for Finance I
- Supervised learning: regression, regularisation, k-NN, SVM
- Decision trees, random forests, gradient boosting
- Feature engineering with financial data; cross-validation and leakage
- Model risk, interpretability and validation
05Data Science and Machine Learning for Finance II
- Unsupervised learning, clustering and dimensionality reduction
- Deep learning: neural networks, sequence models
- Natural language processing for news, filings and sentiment
- Reinforcement learning for trading and hedging
06Fixed Income, Credit and Digital Assets
- Term-structure models: short-rate, HJM and LIBOR/OIS market models
- Bond, swap and option pricing with multiple curves
- Credit risk: structural, reduced-form and copula models; CVA/XVA
- Digital assets and tokenised instruments: pricing, funding rates and perpetual futures
Advanced electives (select two)
Assessment
- Three examinations, after Modules 2, 3 and 4 (open book, applied problems)
- Assignments and coding labs in each module
- Final capstone project: a complete model, strategy or risk engine with a report and a code review
- At Master’s level: a Master’s thesis in the specialisation. At PhD level: a dissertation
Tools
- Python (NumPy, pandas, SciPy, scikit-learn, PyTorch)
- Jupyter, Git and cloud notebooks
- Market data APIs and historical tick data
- Optional: R, C++/Rust for performance-critical code
Who is this programme for?
- Analysts, structurers, risk managers and portfolio managers
- Traders and quant developers
- Actuaries, engineers and data scientists who move into finance
- Graduates in mathematics, physics, computer science, economics or engineering
Next steps
Postgraduate Certificate
PGCert in Business Informatics & Data Science or in Business Administration, specialisation Quantitative Finance (30 ECTS)
Master’s degree
MSc in Business Informatics & Data Science or MBA in Business Administration, specialisation Quantitative Finance (90 ECTS)
PhD
Dissertation in quantitative finance and machine learning (Level 8)
Career options
- Quantitative analyst, quant developer, risk manager
- Portfolio manager, structurer, trader
- Model validation, financial engineering, FinTech product development
Ready for the next step in digital finance?
Apply for a Postgraduate Certificate, a Master’s programme or the PhD. Or talk to us first.