Credential program on the researcher path
Fellowship in Computational Finance
The top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
The Fellowship in Computational Finance trains research, not routine. Its advanced courses are taught at proof level: you derive the models before you use them, estimate them on data, and study the literature that shows where they fail. The dissertation then asks you to do the same for a question of your own.
What does the Fellowship cover?
The first block lays the mathematics, statistics, econometrics, Python, SQL and market knowledge. The second is derivatives pricing: stochastic calculus and differential equations, Black-Scholes, the Greeks, the volatility smile, Heston, numerical methods and interest rate models. The third is quantitative risk management: market, credit and counterparty risk, volatility models, stress tests, Basel capital and model risk. The fourth is machine learning: financial machine learning, deep learning, language models and alternative data, Bayesian methods, regimes and reinforcement learning.
Who is it for?
Quantitative researchers who want formal training, graduate students preparing for research roles, and practitioners with a question they have wanted to study properly. Applicants should be comfortable with university calculus, linear algebra and probability.
The courses in this program
- Mathematics for Quantitative Finance: Calculus and Linear Algebra The calculus, linear algebra, optimization and differential equations that pricing, risk and portfolio models are built on, revised and applied to finance. It also teaches the linear algebra for finance behind portfolios and factor models.
- Probability for Quantitative Finance, with Statistics The probability every quant course assumes: distributions, expectation, conditioning and the limit theorems, with each idea tested on real return data. It also teaches the statistics for finance that every model rests on.
- Time Series Analysis and Financial Econometrics The time series methods behind trading signals and risk forecasts: stationarity, autocorrelation, ARMA models, unit roots, volatility clustering and honest forecast evaluation. It also teaches financial econometrics: regression, its failures and their fixes.
- Python and SQL for Finance A Python for finance course with the Python every later course assumes: loading and cleaning market data with pandas, computing returns and risk with NumPy, plotting, and writing code another person can rerun. It also teaches SQL for finance, so your data lives in a database rather than in files.
- Financial Markets and Instruments A financial markets course for people who will build models of markets: what each instrument is, who trades it, where and how it trades, and what happens between the click and the settled position.
- Algorithmic Trading Foundations: From Idea to Paper to Live An algorithmic trading course on the working basics: how electronic markets match orders, the order types an algorithm uses, three simple strategy families, the costs of trading, and a first backtest you can defend. It also teaches paper trading, and how to decide when a strategy is ready for real money.
- Stochastic Calculus and Stochastic Differential Equations Brownian motion, the Ito integral, Ito's lemma, change of measure and martingale pricing, derived carefully and applied to the models used on derivatives desks. It also teaches stochastic differential equations and how to solve them.
- The Black-Scholes Model and the Option Greeks Option pricing from the ground up: payoffs and no-arbitrage bounds, the binomial model, the replication argument, the Black-Scholes equation and formula, risk-neutral valuation, and numerical pricing in Python. It also teaches the option Greeks and hedging with them.
- Implied Volatility, the Smile and Volatility Trading What option prices say about expected volatility: solving for implied volatility, the smile and skew, the term structure, building and fitting a volatility surface, and the no-arbitrage conditions a surface must satisfy. It also teaches volatility trading: variance swaps, straddles and the volatility risk premium.
- Stochastic Volatility and the Heston Model Stochastic volatility in depth: why volatility needs its own process, the Heston model and its characteristic function, Fourier pricing methods, calibration to a volatility surface, simulation schemes, and rough volatility in outline.
- Numerical Methods in Finance The numerical methods behind every pricing and risk system: Monte Carlo and its variance reduction, finite differences for pricing equations, lattices, Fourier methods, root finding and optimization for calibration, with attention to error and stability.
- Interest Rate Models The models banks use to price options on interest rates: short-rate models from Vasicek to Hull-White, the Heath-Jarrow-Morton framework, the market models behind caps and swaptions, the move from LIBOR to SOFR, and calibration to market prices.
- Bond Pricing, Yield Curves and Mortgage-Backed Securities Bond pricing and the yield curve from first principles to desk practice: price and yield, duration and convexity, bootstrapping and fitting curves, carry and roll-down, and hedging a bond portfolio's interest rate risk. It also teaches mortgage-backed securities and prepayment risk.
- Value at Risk, Expected Shortfall and Stress Testing How value at risk is calculated, tested and misused: historical simulation, variance-covariance and Monte Carlo methods, volatility-weighted variants, time scaling, regulatory backtesting, and what VaR does not tell you. It also teaches expected shortfall, also called CVaR and stress testing and scenario design.
- Model Risk Management and Basel III Capital How financial institutions control the risk of using models: what counts as a model, inventories and tiering, independent validation, conceptual soundness, benchmarking and outcomes analysis, ongoing monitoring, and the governance expected by supervisors. It also teaches Basel III market risk capital.
- GARCH Models, Market Regimes and Bayesian Methods The GARCH model and its family, from Engle's original ARCH to asymmetric and multivariate GARCH: derivation, maximum likelihood estimation, diagnostics and forecasting. It also teaches hidden Markov models for market regimes and Bayesian statistics for estimation under uncertainty.
- Credit Default Swaps, Copulas and Counterparty Risk Credit default swaps from the contract to the model: what is protected and paid, hazard rates and survival curves, bootstrapping a CDS curve with the ISDA standard model, index products, and the basis between bonds and CDS. It also teaches copula models for portfolio credit risk and counterparty credit risk and CVA.
- FRM Exam Preparation: Part I Structured preparation for the FRM Part I exam: the four Part I topic areas taught with worked problems, timed practice sets, and a mock exam. Independent preparation; not affiliated with or endorsed by GARP.
- Machine Learning for Trading Applying machine learning to trading problems without fooling yourself: building features and labels from market data, linear, tree and neural network models, time-series cross-validation, feature importance, and turning predictions into a tested strategy.
- Financial Machine Learning: Deep and Reinforcement Learning The research methods developed for machine learning on financial data: sampling with information-driven bars, triple-barrier labels and meta-labeling, sample weights for overlapping outcomes, purged cross-validation, feature importance, and statistics of backtest overfitting. It also teaches deep learning for finance and reinforcement learning for trading.
- Alternative Data and NLP for Finance Alternative data and natural language processing as funds use them: turning news, filings and earnings calls into signals with language models, judging satellite, card and web data, and testing whether any of it adds return once costs and timing are honest.
What every course contains
Every paid course comes with the first seven and the certificate, whichever level it sits at. The proctored exam and the credential come when the course is taken as part of a Certificate, Diploma or Fellowship. Everything is self-paced: the material is yours to study in any order, at any hour, for the whole of the license.
- Workbooks. Written to train the thinking patterns and the important concepts of the course, with worked problems and the reasoning behind each answer, so that you learn to think the way the subject is thought.
- Audio edition. Focused, well-presented recordings with explainers and examples of the material, made to be interesting, relevant and entertaining, so that the general subject can be learned away from the desk.
- Notes. Pre-produced notes with every fact the course relies on, written out in full, so that nothing has to be transcribed from the films or the audio.
- Cheat sheet. One sheet with every important learning concept of the course on it, to keep beside you during the work and to revise from before the exam.
- Named sources. Every fact in the notes names its source, the paper, the book or the dataset, for anyone who wants to dig in further.
- Explainer films. Focused films, in the style of the sample films on the catalog page, that walk through the most difficult concepts of the course with visual explanations of a high standard.
- Transcript. A full transcript of every film and every audio recording, searchable and quotable.
- Proctored exam. Taken as part of a Certificate, Diploma or Fellowship, the course ends in a proctored multiple-choice questionnaire, with a pass mark of 60% and three retakes included. A course taken on its own is assessed by its coursework and has no exam.
- Verifiable certificate. A certificate of completion that a third party can verify: an employer sends the code on it to the credentials office and receives written confirmation of the course, the date and the result.
- Credential of the program. The Certificate, Diploma or Fellowship of the program, track or path the course belongs to, awarded when every course in it is passed. Which one depends on the program you choose.
What should you know before you start?
The courses in this program build on the courses below. They are not part of the program and not included in its price. If you have not studied this material, take them first. Admissions can tell you whether your background already covers them: the academy office.
How does the Fellowship dissertation work?
You propose a research question in computational finance, agree its scope in writing with a faculty supervisor, and write a dissertation of 10,000 to 15,000 words with reproducible code and data. Supervision is given in writing: comments on the proposal, on two drafts and on the final text. The dissertation is examined in writing by two faculty members, one of whom did not supervise it.
What does the credential record?
It records that you passed the proctored exam of every course listed on this page and the capstone, assessed by the Institute's faculty. The credential is issued by the Institute. Each one carries a verification code that an employer can check with the credentials office.
Are there scholarships and financial aid?
A small number of need-based awards are made each intake to students who could not otherwise study, on documented evidence of financial hardship. Most applications are declined. Before you apply, look at what this program already saves you: it costs less than its courses bought alone, and full-fee students are what make every award possible. Read the requirements before you apply.
Questions applicants ask
How does the Fellowship compare with a master's in computational finance?
It is shorter and denser. A master’s spreads its content over lectures and reading weeks; the Fellowship teaches the core of each subject in tight, edited lessons and leaves the hours for your own research. It is studied online at your own hours, every course ends in a proctored exam, and the dissertation is examined by two faculty members.
Who supervises the dissertation?
A member of the Institute’s faculty, matched to the topic you propose. The question must fall within the faculty’s areas of expertise; the supervisor confirms this in writing before you start, and all supervision is given in writing.
How long does the Fellowship take?
232 hours of material across the twenty-one courses, each hour with its audio edition and workbook, and a capstone of 80 hours of your own work. The license runs 24 months, so you set the pace around a job.