Oxford Certificate Programmes · Worcester College
Computational finance is the practice of pricing, hedging, and measuring markets with code; financial technology is the set of innovations reshaping the infrastructure of those markets and the nature of money itself. This two-week course joins the two.
Week One is a rigorous, hands-on introduction to computational finance in Python, following a single arc (describe a market, price it by no-arbitrage, then try to predict it) using a century of real cross-asset data. Week Two turns to FinTech and digital money, examining market microstructure, cryptoassets, stablecoins, central bank digital currencies, and payment systems as applied financial and monetary economics rather than as a tour of headlines.
Students learn to compute returns, volatility, correlation, and drawdown and to read the stylised facts of financial data; to price forwards, futures, and options by no-arbitrage and to construct and judge hedges; to read market expectations from the yield curve; and to build and stress-test portfolios, discovering why a backtest that looks brilliant collapses once data leakage, look-ahead bias, and transaction costs are handled honestly. The second week asks what counts as a financial asset, what counts as money, and who controls the rails of finance. Throughout, two disciplines stay central: the difference between prediction and causation, and the test of whether a digital innovation actually delivers an economic function or merely relabels it.
Teaching method. Students are taught according to the Oxford Socratic model, where class participation is central. Week One combines lectures, guided discussion, and hands-on Python labs; Week Two combines lectures with structured discussion, debate, and case work. Each idea is built intuition-first and made concrete with a small worked example before any formula: plain idea, then a little mathematics, then code.
Prerequisites. No prior finance or programming experience is assumed. Comfort with basic algebra and an interest in markets is enough; some familiarity with Python helps with the Week One labs but is not required, and the core ideas are built from the ground up so that motivated newcomers can follow.
Assessment. There is a Friday assessment at the end of each week: a technical assessment on the computational-finance material at the close of Week One, and a policy and design assessment on digital markets and money at the close of Week Two. The emphasis is on interpreting one's own results, defending modelling choices, and critiquing flawed analyses, rather than on reproducing code or memorised facts.
| Day | Topic | Focus |
|---|---|---|
| Week One · Computational Finance with Python | ||
| Mon 17 Aug | Measure the market | Prices versus returns; volatility, correlation, and drawdown; the stylised facts of financial data; and why every measurement is a choice. |
| Tue 18 Aug | Hedge it, and read expectations | Forward and futures pricing by no-arbitrage; the minimum-variance hedge ratio and basis risk; and reading rate expectations from the yield curve. |
| Wed 19 Aug | Price the optionality | Option payoffs and put–call parity; Black–Scholes as a pricing engine; delta as a hedge ratio; and implied volatility as the market's forecast. |
| Thu 20 Aug | Decide, and watch prediction fail | Portfolios and the covariance matrix; Value-at-Risk; factor models; and how leakage, look-ahead, and costs make a backtest lie. |
| Fri 21 Aug | Assessment | Week-one technical assessment. |
| Week Two · FinTech, Digital Markets, and Digital Money | ||
| Mon 24 Aug | How markets process information | Market microstructure and liquidity; event studies; and how central-bank announcements and futures encode and move expectations. |
| Tue 25 Aug | Crypto as a financial market | Cryptoassets as market structure, not ideology; volatility, correlation, and custody; derivatives and funding; and institutionalisation. |
| Wed 26 Aug | Digital money: stablecoins, CBDCs, tokenisation | What makes a dollar a dollar; the money hierarchy; reserve and run risk; sovereignty; and the US, EU, and UK regulatory frameworks. |
| Thu 27 Aug | Payments, open finance, and AI | Card, instant, and cross-border rails; settlement and ISO 20022; open finance; and AI in financial services, model risk, and concentration. |
| Fri 28 Aug | Assessment | Week-two policy and design assessment. |
Week One · Computational Finance with Python
The opening session builds the vocabulary the whole course reuses: prices versus returns, log returns, volatility, correlation, and drawdown, computed in Python on real data. We meet the stylised facts that make financial series unlike ordinary data (fat tails, volatility clustering, and non-stationarity) and the ways data can mislead, from survivorship bias to unbalanced panels, before building a multi-asset risk dashboard.
Key idea: a market is something you measure, and even the measurement is full of choices.
This session prices forwards and futures by no-arbitrage through the cost of carry, then turns to hedging: the minimum-variance hedge ratio, which is simply a regression slope, with basis risk as everything the regression leaves behind. We then read the market's expectations of future interest rates directly from the shape of the yield curve, building the bridge to the second week's work on central-bank announcements.
Key idea: a futures price is spot plus carry, a hedge is a regression, and the curve is the market's forecast.
We introduce options through their payoffs and the no-arbitrage logic of put–call parity, then present Black–Scholes as a pricing engine with a single input, volatility, that cannot be observed. Delta appears as a hedge ratio that moves with the market, and implied volatility (the VIX) appears as the market's own forecast of risk and the price of insurance.
Key idea: an option is a position in volatility: the one input you cannot observe is what the price is really about.
The first week culminates in portfolio construction and risk measurement (diversification, the covariance matrix, and Value-at-Risk) and in factor models that reveal how much apparent skill is merely exposure. We then build a trading strategy that looks superb and dismantle it step by step, watching its performance collapse as data leakage, look-ahead bias, and transaction costs are handled honestly.
Key idea: a backtest is a story you fit to the past; the question is whether it reflects a real, repeatable edge or an artefact of how you built it.
Week Two · FinTech, Digital Markets, and Digital Money
The second week opens with market microstructure (order books, the bid–ask spread, liquidity, and price discovery) and with the event-study methods used to measure how fast information enters prices. We focus on central-bank announcements: how futures and the yield curve encode the expected path of policy, and how a narrow window around a scheduled announcement identifies the surprise and its effect.
Key idea: prices aggregate information, and narrowing the window around an announcement is how you identify what the news actually did.
This session treats cryptoassets as a financial market rather than as ideology: scarcity and the valuation problem of an asset with no cash flows; exchanges, custody, and counterparty risk; spot, perpetual futures, and funding rates; and the evidence on volatility, correlation, and institutionalisation through exchange-traded products. A structured debate asks whether crypto is best understood as an asset class, a payment system, or a speculative technology.
Key idea: judge crypto by the evidence (volatility, correlation, and market structure), not by ideology.
The intellectual centre of the course asks what money is and what makes a dollar a dollar: the money hierarchy, singleness, and the engineered nature of par convertibility. We examine fiat-backed and algorithmic stablecoins and their run risk, the design choices and trade-offs of central bank digital currencies, and tokenised deposits and assets, alongside the live regulatory framework in the United States, the European Union, and the United Kingdom.
Key idea: money is an institutional promise held together by par convertibility; the test for any digital form is whether a dollar still equals a dollar.
The closing session studies the rails of finance: card networks, instant and cross-border payments, settlement finality, and the network effects that make payment systems natural near-monopolies, together with ISO 20022 and open finance. It ends with AI in financial services (where it is used, and how it tends to amplify existing risks of opacity, herding, and concentration) and a design challenge that draws the whole course together.
Key idea: technology re-plumbs finance, but the test is always whether the digital version delivers the economic function or merely relabels it.
A mix of standard texts and freely available sources; links are provided where an item is openly available online.